POLITECNICO DI MILANO

Facoltà di Ingegneria Industriale

Corso di Laurea in

Ingegneria Energetica

The effect of automatic control on building energy use for a smart city

Relatore: Prof. Francesco Causone

Co-relatore Prof. Luigi Pietro Maria Colombo

Tesi di Laurea di:

Luca Prosdocimi Matr. 799608

Anno Accademico 2013 - 2014

I

Ringraziamenti

Un primo e fondamentale grazie deve andare al Professor Causone, per la

sua grande e continua disponibilità nei miei confronti e per la fiducia

concessami in questi sei mesi di lavoro, un secondo grazie è per Amin, che

mi ha introdotto al software DesignBuilder e mi ha dato una grande mano

con le logiche dei modelli infine un ultimo grazie deve andare al Professor

Colombo per avermi fornito preziosi consigli su come migliorare il lavoro.

.

Il ringraziamento più sentito deve però andare alla mia famiglia; a partire

da mio padre e a mia madre per avermi supportato e talvolta sopportato in

questi 5 anni, senza i loro sacrifici non potrei essere qui in questo

momento. A mio fratello per avermi saputo trasmettere buon umore. A mia

nonna che mi ha foraggiato ogni volta che ero a casa durante le sessioni

d’esami.

Ringrazio inoltre anche Serena, capace di sentitami parlare per ore parlare

di argomenti decisamente poco interessanti e aiutato nei momenti di

maggior scoramento e difficoltà.

Ringrazio infine i miei due compagni che son stati insieme a me dal primo

all’ultimo di questi anni accademici, Walter e Gianluca.

Infine un ringraziamento speciale va a mio nonno Ezio che so avrà un

occhio di riguardo da lassù.

II

III

Contents

Abstract ................................................................................................ V

Sommario ........................................................................................... VII

Sommario Esteso ................................................................................. IX

List of Tables ..................................................................................... XIII

List of Figures .................................................................................. XVII

1 Smart City .......................................................................................... 1 1.1 Introduction ................................................................................... 1 1.2 Definitions ...................................................................................... 4 1.3 Areas of interest ............................................................................. 7

2 Definitions and characteristics of high performance buildings.......... 19 2.1 Buildings ...................................................................................... 19

2.1.1 Passive building ............................................................................................... 22 2.1.2 Zero Energy Buildings (ZEB) ........................................................................ 27 2.1.3 Smart Buildings ............................................................................................... 32

2.2 Distributed energy production ...................................................... 37 2.2.1 Technologies used in Distributed Energy ...................................................... 38

3 The CONCERTO project experience ................................................ 47 3.1 Introduction ................................................................................. 47

3.1.1 CONCERTO buildings (Demand Side) ......................................................... 50 3.1.2 CONCERTO energy supply units (Supply Side) .......................................... 53

3.2 Introduction to CONCERTO Premium Technical Monitoring

Database ............................................................................................ 59 3.2.1 Buildings indicators ........................................................................................ 61 3.2.2 Energy supply units indicators ...................................................................... 65

3.2.2.1 Focus on Technical Indicators of Buildings and ESU................................. 68 3.2.3 City indicators ................................................................................................. 70

3.3 Case studies: Energy and Urban Regeneration of the Arquata

District in the city of Torino ............................................................... 71 3.3.1 Presentation of the case studies ...................................................................... 71 3.3.2 Refurbishment of the ACT building .............................................................. 72 3.3.3 Refurbishment of social housing buildings ................................................... 74 3.3.4 District heating and cogeneration .................................................................. 76 3.3.5 Impact on the Arquata district ...................................................................... 78

3.4 Analysis of CONCERTO database ............................................... 78 3.5 Discussion ..................................................................................... 83

4 The effect of automatic control on building energy need/use ............. 85 4.1 Automatic control in buildings ..................................................... 85

4.1.1 Heating/Cooling systems ................................................................................. 89 4.1.2 Lighting Systems ............................................................................................. 95 4.1.3 Ventilation System .......................................................................................... 99

IV

4.1.4 Blind System .................................................................................................. 103 4.2 Energy simulation of a case study ............................................... 104

4.2.1 Effect of automatic control of lighting in a case study ............................... 110 4.2.1.1 Cost esteem of the lighting and solar screen control system ................124

4.2.2 Calculation to create a ZEB on annual and monthly base ........................ 126

5 Conclusions ................................................................................... 133

ANNEX I .......................................................................................... 135

ANNEX II ........................................................................................ 151

Bibliography ..................................................................................... 159

V

Abstract

Cities occupy approximately only 2% of earth ground, but 55% of the

world population already live in urban areas. Within the European Union,

cities are responsible for about 70% of the overall primary energy

consumption. In this context the concept of Smart City has been the subject

of increasing attention. Smart Cities are characterised by an intense use of

Information and Communication Technologies (ICT), which in various

urban domains, help cities making better use of their resources. The first

step of this thesis was to analyse the literature about the Smart City

concept focusing on energy aspects. In particular we concentrate on

buildings’ energy aspects, because they are responsible for a significant

share of energy use worldwide and they are responsible for 40% of total

European energy consumption and for 36% of Green House Gases (GHG)

emissions. Although ICT is identified as key element of Smart Cities, it has

been observed that most of the existing projects do not consider the effects

of Building Automation and Control System (BACS) on buildings energy

uses. They limit instead to building envelope and systems renovation, as

we showed in Chapter 3, where the CONCERTO project is studied and in

particular the Arquata district in Turin is analysed in detail as a reference

case study.

In the second part of the thesis, the different automation systems used in

buildings to control heating, cooling, lighting, ventilation, and solar

shading have been analysed in detail. The literature analysis was followed

by a practical application where we studied the effects of lighting and blind

control on a kindergarten located in Milan, using the energy simulation

software Energy Plus with DesignBuilder interface. The study was

conducted on two different models of the building: before and after the

retrofit of the opaque and transparent envelope, to observe the difference of

applying an automatic control on a building with low or high energy

performance. Finally the possibility to make the retrofitted kindergarten a

Zero Energy Building (ZEB) was investigated, observing and discussing

the differences among different calculation approaches present in the

literature.

Keywords: Smart Building; BACS; Building Automation; Zero Energy

Building

Conventions: All the numbers included in the thesis are shown with

comma “,” as decimal separator.

VI

VII

Sommario

Le città occupano approssimativamente solo il 2% della superficie

terrestre, ma il 55 % della popolazione vive in aree urbane. Nell’Unione

Europea, le città sono responsabili di circa il 70% del consumo di energia

primaria. In questo contesto il concetto di Smart City è divenuto oggetto di

una sempre maggior attenzione. Le Smart Cities sono caratterizzate da un

intenso uso di Information and Communication Technologies (ICT), che in

diversi domini urbani aiutano le città a far miglior uso delle proprie risorse.

Il primo passo di questa tesi è stato analizzare la letteratura riguardo il

concetto di Smart City focalizzandosi sugli aspetti energetici. In particolare

ci si è concentrati sugli aspetti energetici degli edifici, perché sono

responsabili di una parte significativa dell’uso di energia in tutto il mondo

e sono responsabili del 40% del consumo energetico europeo e del 36%

delle emissioni di gas serra (GHG).

Sebbene l’ICT è identificato come un elemento chiave per le Smart Cities,

è stato osservato come la maggior parte dei progetti esistenti non considera

l’effetto dei Building Automation and Control System (BACS) sugli usi

energetici degli edifici. La maggior parte dei progetti in corso si limitava

infatti, al rinnovamento dell’involucro e degli impianti degli edifici

escludendo l’uso di sistemi di controllo automatici avanzati, come mostrato

nel Capitolo 3, dove il progetto CONCERTO è stato analizzato

accuratamente. In particolare si è scelto come caso studio di riferimento il

distretto Arquata di Torino per il quale era disponibile un maggior numero

di dati.

Nella seconda parte della tesi i differenti sistemi di automazione usati

negli edifici per controllare riscaldamento, raffrescamento, illuminazione,

ventilazione, ombreggiamento solare sono stati studiati in dettaglio.

L’analisi della letteratura è stata seguita da una applicazione pratica dove

abbiamo studiato gli effetti del controllo di illuminazione e schermature

solari su un asilo di Milano, utilizzando il software Energy Plus con

interfaccia DesignBuilder. Lo studio è stato condotto su due differenti

modelli dell’edificio: prima e dopo la ristrutturazione dell’involucro opaco

e trasparente, per osservare le differenze di una possibile applicazione di

un sistema di controllo automatico su un edificio a basse o ad alte

performance energetiche. Infine la possibilità di rendere l’asilo ristrutturato

uno Zero Energy Building (ZEB) è stata investigata, osservando e

discutendo le differenze tra i diversi approcci di calcolo presenti in

letteratura.

Parole chiave: Smart Building; BACS; controlli automatici; Zero Energy

Building

VIII

IX

Sommario Esteso

Le città occupano approssimativamente solo il 2% della superficie

terrestre, ma il 55 % della popolazione vive in aree urbane. Nell’Unione

Europea, le città sono responsabili di circa il 70% del consumo di energia

primaria. In questo contesto il concetto di Smart City è divenuto oggetto di

una sempre maggiore attenzione. Le Smart Cities sono caratterizzate da un

intenso uso di Information and Communication Technologies (ICT), che in

diversi domini urbani, quali possono essere i trasporti, l’educazione,

l’accesso alle informazioni, i consumi energetici etc., aiutano le città a far

miglior uso delle proprie risorse.

Nel capitolo 1 abbiamo osservato come il concetto di Smart City è ancora

emergente e non esiste una definizione ben delineata in letteratura. Molto

spesso, inoltre, il concetto di Smart City è confuso con altri concetti simili,

quali possono essere quello di Intelligent City, Digital City, Eco City e

Sustainable City. Nella tesi abbiamo fatto nostra una definizione

dell’Unione Europea, in cui vengono individuati sei pilastri sui quali una

Smart City deve essere fondata: Smart Economy, Smart People, Smart

Governance, Smart Mobility, Smart Environment e Smart Living.

Dopo aver analizzato i sei pilastri e le varie interpretazioni date in

letteratura, ci siamo accorti di come l’energia sia collegata con tutti i

pilastri, nonostante non sia mai posta al centro delle definizioni di Smart

City. I temi energetici legati al concetto di Smart City sono ovviamente

molti, tra di loro interconnessi e difficili da trattare tutti

contemporaneamente. In questa tesi abbiamo pertanto deciso di affrontare

il tema dei consumi energetici degli edifici all’interno della Smart City,

focalizzando le nostre analisi sull’uso dei controlli automatici nel settore

edilizio e sulle relative possibili conseguenze in termini di consumo

energetico.

Il capitolo 2 della tesi descrive gli edifici ad alto rendimento e in

particolare vengono riportate le definizioni di letteratura di Passive

Building, Zero Energy Building e Smart Building.

L’espressione passive building, ovvero edificio passivo, si riferisce a

costruzioni che utilizzano forzanti climatiche esterne per riscaldare,

raffreddare o illuminare un edificio. Insieme alla definizione di passive

building sono anche state analizzate le diverse tipologie di standard per

edifici passivi presenti in letteratura. In particolare è stato analizzato nel

dettaglio lo standard Passivhaus, considerato lo standard internazionale più

influente con almeno 25000 progetti certificati in Europa e lo standard

italiano Casaclima.

In seguito sono stati analizzati i nearly Zero Energy Building (nZEB).

Abbiamo analizzato la Directive on Energy Performance of Buildings

X

(EPBD) adottata nel Maggio 2010 (recast version), che stabilisce che tutti i

nuovi edifici pubblici dovranno essere nZEB a partire dal 2018, mentre a

partire dal 2020 tutti gli altri tipi di edifici. Anche la definizione di nZEB

non è chiaramente definita, in particolare ci sono opinioni contrastanti

riguardo il tipo di fonti da prendere in considerazione nel bilancio

energetico, se questo bilancio deve essere effettuato su base annuale

oppure mensile e sui confini fisici da utilizzare nel calcolo (i muri

dell’edificio, il sito di costruzione, il quartiere, etc.). È stata illustrata

inoltre la definizione di nZEB data dall’EPBD stessa che considera uno

nZEB come un edificio che, come risultato di un alto livello di efficienza,

consuma la stessa energia primaria dell’energia prodotta per risorse

rinnovabili su base annua.

Sono stati infine analizzati i così detti Smart Buildings. Anche per questi

ultimi è stato osservato che non esiste una definizione comunemente

accettata in letteratura. Indubbiamente uno Smart Building si caratterizza

per l’uso diffuso di molti sistemi di controllo automatico, ma questi devono

essere asserviti allo scopo primario di ridurre il consumo energetico

dell’edificio e migliorare le condizioni di benessere e sicurezza degli

occupanti. Un edifico che aumentando i sistemi di automazione non porta

vantaggi ai consumi energetici ed al benessere degli occupanti non può

essere considerato uno Smart Building.

Nel finale del capitolo 2 ci si è concentrati su un breve riepilogo dei

principali sistemi di generazione distribuita che possono essere adatti a

servire i fabbisogni energetici degli edifici (fotovoltaico, solare termico,

solare termodinamico, pompe di calore geotermiche, piccoli impianti di

cogenerazione, eolico e micro eolico etc.). Sono stati inoltre messi in

risalto i vantaggi e i punti di forza delle generazione distribuita.

Nel capitolo 3 è stato analizzato il progetto europeo CONCERTO.

CONCERTO è una iniziativa Europea dell’ambito delle Smart Cities, che

punta a dimostrare come una ottimizzazione dei distretti e dei quartieri

come un insieme unitario possa essere molto più favorevole dal punto di

vista economico che ottimizzare ogni singolo edificio singolarmente.

L’iniziativa comprende 22 progetti in 58 città in 23 paesi all’interno

dell’Unione Europea. All’interno del capitolo diversi tipi di progetti sono

illustrati brevemente insieme ai diversi tipi di intervento e tecnologie

utilizzate.

In particolare è esposto nel dettaglio il progetto POLYCITY di Arquata, un

quartiere di Torino. Il progetto è stato analizzato utilizzando anche il

CONCERTO Technical Monitoring Database, il database ufficiale dei

progetti CONCERTO. Purtroppo durante lo svolgimento delle analisi, il

database si è rivelato essere un mezzo minato da profondi deficit strutturali

e quindi utile soltanto per una prima analisi approssimativa del progetto.

Nella discussione di fine capitolo sono stati riportati tutti gli aspetti

XI

negativi del database sia dal punto di vista dei contenuti che dal punto di

vista dell’utilizzo.

Nell’ultimo capitolo è stata introdotta la definizione di Building

Automation and Control System (BACS) e la sua evoluzione nel corso

degli anni. È stato inoltre descritto in breve come funziona e come è

formato un sistema di building automation (BA), e la normativa vigente

che descrive l’influenza dei controlli automatici sui consumi energetici

degli edifici e sulla loro efficienza energetica (Standard EN 15232).

Sono infine stati descritti i principali sistemi di controllo per quanto

riguarda il riscaldamento, il raffrescamento, l’illuminazione la

ventilazione, e l’ombreggiamento solare, riportando alla fine di ogni

paragrafo una tabella riassuntiva presa dalla norma di riferimento per avere

una maggior chiarezza delle varie tipologie di controllo.

L’analisi della letteratura è stata seguita da una applicazione pratica in cui

gli effetti del controllo dei sistemi di illuminazione e schermature solari di

un asilo di Milano sono stati studiati, utilizzando il software Energy Plus

con interfaccia DesignBuilder. Lo studio è stato condotto su due differenti

modelli dell’edificio: prima e dopo la ristrutturazione dell’involucro opaco

e trasparente, per osservare le differenze di una possibile applicazione di

un sistema di controllo automatico su un edificio a basse o ad alte

performance energetiche. Sono stati descritti tutti i dati iniziali del

problema e le condizioni al contorno, riportando in appropriate tabelle

riassuntive tutti i dati relativi al caso studio. Sono stati introdotte in seguito

le diverse logiche e tipologie di controllo.

Sono state presentate le 12 diverse simulazioni oggetti dello studio ed i

conseguenti risultati sia dal punto di vista del fabbisogno energetico

dell’edificio, che del consumo di energia primaria. Sono stati analizzati i

casi del solo controllo del sistema di illuminazione, del solo controllo

solare e dell’unione dei due controlli. Tutte le analisi sono state condotte

per mantenere lo stesso livello di comfort termico, valutato attraverso la

temperatura operativa come indicatore, ma non considerano la possibilità

di abbagliamento e del conseguente discomfort visivo.

Sono stati infine fatti i calcoli del tempo di ritorno del sistema di

automazione sia sull’edifico esistente che sull’edificio ristrutturato.

Infine la possibilità di rendere l’asilo ristrutturato uno Zero Energy

Building (ZEB) è stata investigata, osservando e discutendo le differenze

tra i diversi approcci di calcolo presenti in letteratura.

XII

XIII

List of Tables

Table 1.1 IBM vision of smarter cities [6]. .................................................. 3

Table 1.2 Characteristics and factors of the Smart City [16] ....................... 8

Table 1.3 “hard” and “soft” domains and their respectively sub-domain

and a short description [14]. ....................................................................... 12

Table 1.4 Europe 2020 targets for the EU as a whole [13]. ....................... 15

Table 1.5 The number of cities with initiatives directly or indirectly

aligned with Europe 2020 targets [13]........................................................ 16

Table 2.1 Examples of component quality and construction suitable to

reach the Passivhaus standard (“recommended”) and best available

components (“best practice”) [25]. ............................................................. 25

Table 2.2 ZEB renewable energy supply option hierarchy [29] ................ 28

Table 2.3 Building energy consumption and GHG emission with saving

potential in selected countries and world [22] ............................................ 35

Table 2.4 Comparison of most common distributed energy sources [46] . 38

Table 2.5 Technical features of small-scale CHP devices [50] ................. 44

Table 3.1 SESAC project facts and results [57] ....................................... 49

Table 3.2 Selected examples of retrofitting projects within CONCERTO 51

Table 3.3 58 cities of CONCERTO Database ........................................... 60

Table 3.4 Technical indicators for buildings ............................................. 61

Table 3.5 Environmental indicators for buildings ..................................... 63

Table 3.6 Economic indicators for buildings ............................................. 64

Table 3.7 Economic-environmental indicators for buildings..................... 65

Table 3.8 Technical indicators for ESU ..................................................... 66

Table 3.9 Environmental indicators for ESU ............................................. 67

Table 3.10 Economic indicators for ESU .................................................. 67

Table 3.11 Economic-Environmental indicators for ESU ......................... 68

Table 3.12 City indicators .......................................................................... 70

Table 3.13 ATC building specification ...................................................... 73

Table 3.14 ATC building PV generators .................................................. 74

Table 3.15 Social housing buildings specification..................................... 75

Table 3.16 Social house buildings PV generators ..................................... 75

Table 3.17 CHP main features ................................................................... 77

Table 3.18 Sustainability impact of the project, calculated value [53] ...... 78

Table 3.19 CONCERTO Database technical indicators for ATC building

.................................................................................................................... 79

Table 3.20 CONCERTO Database technical indicators for social housing

building ....................................................................................................... 80

Table 3.21 CONCERTO Database environmental indicators for social

housing building ......................................................................................... 80

Table 3.22 Energy output CHP .................................................................. 80

XIV

Table 3.23 Annual electricity output of all PV units ................................. 82

Table 3.24 CO2 emissions reduction for Arquata ESU ............................. 82

Table 3.25 Practical and content lacks CONCERTO Technical Monitoring

Database ...................................................................................................... 83

Table 4.1 Heating/Cooling automatic control in buildings: summary table

Norm EN 15232 ......................................................................................... 93

Table 4.2 Effects of different parameters on occupancy control

performance [75] ........................................................................................ 96

Table 4.3 Comparison between daylight-linked switching and dimming

controls [75] ................................................................................................ 98

Table 4.4 Lighting automatic control in buildings: summary table Norm

EN 15232 .................................................................................................... 98

Table 4.5 Ventilation automatic control in buildings: summary table Norm

EN 15232 .................................................................................................. 101

Table 4.6 Blind automatic control in buildings: summary table from Norm

EN 15232 .................................................................................................. 103

Table 4.7 Data of the building envelope before and after the retrofitting

works......................................................................................................... 104

Table 4.8 Initial data for the simulation ................................................... 106

Table 4.9 Rooms’ different schedule ....................................................... 108

Table 4.10 boundary condition of the simulation .................................... 109

Table 4.11 T12 Fluorescent characteristics.............................................. 111

Table 4.12 LED characteristics ................................................................ 111

Table 4.13 Monthly consumption for case 1 (energy use and need) ....... 113

Table 4.14 Total consumption of case 1 and case 2 and percentage

variation .................................................................................................... 114

Table 4.15 Total consumption of case 1 and case 5 and percentage

variation .................................................................................................... 114

Table 4.16 Total consumption of case 6 and percentage variation between

the previous case ....................................................................................... 114

Table 4.17 Annual primary energy consumption..................................... 115

Table 4.18 Monthly consumption for case 9 (energy use and need) ....... 120

Table 4.19 Total consumption of case 9 and case 10 and percentage

variation .................................................................................................... 121

Table 4.20 Total Primary energy consumption of case 9 and case 10 .... 121

Table 4.21 Comparison between case 1 and case 12c (primary energy use)

.................................................................................................................. 124

Table 4.22 Main necessary components’ number and cost to create a

building automation system. ..................................................................... 124

Table 4.23 LED’s number and prices ..................................................... 125

Table 4.24 Type, Number and price for the buildings’ ceiling lights ..... 125

Table 4.25 Pay-back time for the existing building ................................. 126

Table 4.26 Monthly data for case 9 and fp=1 .......................................... 128

XV

Table 4.27 Monthly data for case 9 and fp=2,18 ..................................... 129

Table 4.28 Monthly data for case 9 and fp=1 (ZEB on monthly bases) .. 129

Table 4.29 Recapping of necessary PV panels to create a ZEB in different

case ........................................................................................................... 131

Table AI.1 Monthly consumption for case 1 (energy use and need) ....... 135

Table AI.2 Monthly consumption for case 2 (energy use and need) ....... 136

Table AI.3 Monthly consumption for case 3a (energy use and need) ..... 137

Table AI.4 Monthly consumption for case 3b (energy use and need) ..... 137

Table AI.5 Monthly consumption for case 3c (energy use and need) ..... 138

Table AI.6 Monthly consumption for case 4a (energy use and need) ..... 138

Table AI.7 Monthly consumption for case 4b (energy use and need) ..... 139

Table AI.8 Monthly consumption for case 4c (energy use and need) .... 139

Table AI.9 Monthly consumption for case 5 (energy use and need) ....... 140

Table AI.10 Monthly consumption for case 6 (energy use and need) ..... 141

Table AI.11 Monthly consumption for case 7a (energy use and need) ... 142

Table AI.12 Monthly consumption for case 7b (energy use and need) ... 142

Table AI.13 Monthly consumption for case 7c (energy use and need) ... 143

Table AI.14 Monthly consumption for case 8a (energy use and need) ... 143

Table AI.15 Monthly consumption for case 8b (energy use and need) ... 144

Table AI.16 Monthly consumption for case 8c (energy use and need) ... 144

Table AI.17 Monthly consumption for case 9 (energy use and need) ..... 145

Table AI.18 Monthly consumption for case 10 (energy use and need) ... 146

Table AI.19 Monthly consumption for case 11a (energy use and need) . 147

Table AI.20 Monthly consumption for case 11b (energy use and need) . 147

Table AI.21 Monthly consumption for case 11c (energy use and need) . 148

Table AI.22 Monthly consumption for case 12a (energy use and need) 148

Table AI.23 Monthly consumption for case 12b (energy use and need) . 149

Table AI.24 Monthly consumption for case 12c (energy use and need) . 149

Table AII.1 Monthly data for case 9 and fp=1 (annual based ZEB) ....... 151

Table AII.2 Monthly data for case 9 and fp=2,18 (annual based ZEB) .. 151

Table AII.3 Monthly data for case 9 and fp=1 (monthly based ZEB) .... 152

Table AII.4 Monthly data for case 9 and fp=2,18 (monthly based ZEB)152

Table AII.5 Monthly data for case 10 and fp=1 (annual based ZEB) ..... 153

Table AII.6 Monthly data for case 10 and fp=2,18 (annual based ZEB) 153

Table AII.7 Monthly data for case 10 and fp=1 (monthly based ZEB) .. 154

Table AII.8 Monthly data for case 10 and fp=2,18 (monthly based ZEB)

.................................................................................................................. 154

Table AII.9 Monthly data for case 11a and fp=1 (annual based ZEB) ... 155

Table AII.10 Monthly data for case 11a and fp=2,18 (annual based ZEB)

.................................................................................................................. 155

Table AII.11 Monthly data for case 11a and fp=1 (monthly based ZEB)

.................................................................................................................. 156

XVI

Table AII.12 Monthly data for case 11a and fp=2,18 (monthly based ZEB)

.................................................................................................................. 156

Table AII.13 Monthly data for case 12c and fp=1 (annual based ZEB) . 157

Table AII.14 Monthly data for case 12c and fp=2,18 (annual based ZEB)

.................................................................................................................. 157

Table AII.15 Monthly data for case 12c and fp=1 (monthly based ZEB)

.................................................................................................................. 158

Table AII.16 Monthly data for case 12c and fp=2,18 (monthly based ZEB)

.................................................................................................................. 158

XVII

List of Figures

Figure 1.1 Percentage of EU population living in urban areas, 1950-2050

[1]. ................................................................................................................. 1 Figure 1.2 Global CO2 emissions (metric tons), 1990,2010 and 2030,

urban/non-urban Source U.S Energy Information Administration Annual

Outlook 2008. ............................................................................................... 2 Figure 1.3 Smart City blueprint [4]. .......................................................... 11 Figure 1.4 Percentage of development of Smart City project [14]. ........... 14 Figure 1.5 European initiative on the Smart Cities technology roadmap

(Source European Commision)................................................................... 16 Figure 2.1 Building energy consumption in selected country [22]............ 19 Figure 2.2 Primary energy use in United States commercial and residential

buildings in 2010 [21]. ................................................................................ 20 Figure 2.3 Illustration of the 5 Passivhaus principles. ............................... 24 Figure 2.4 Comparison of the measured energy consumption of all

CHEPHEUS project with the corresponding values of orfinary, newly

erected buildings according to present stadards [25]. ................................. 26 Figure 2.5 Classes for Casaclima certification. (Casaclima oro, Casaclima

A, Casaclima B) .......................................................................................... 26 Figure 2.6 Diagram of the ZEB approach. Passive design strategies are an

essential aspect to reduce the amount of energy required by the building

[24]. ............................................................................................................. 29 Figure 2.7 SDE 2012 houses passive strategies and other energy efficiency

solutions [27]. ............................................................................................. 31 Figure 2.8 Smart energy building for Morvaj et al. [19] ........................... 34 Figure 2.9 Expected reduction in total emissions of 𝐂𝐎𝟐 with ICT

technologies [41] ........................................................................................ 36 Figure 2.10 Total ICT-enabled smart buildings abatement expanded [41]

.................................................................................................................... 36 Figure 2.11 Installed buildings sector DG capacity in Annual Energy

Outlook 2013 Reference case (Gigawatts) [45].......................................... 38 Figure 2.12 Trends in conversion efficiencies for various solar cell

technologies [21] ........................................................................................ 39 Figure 2.13 Applications of glazed and evacuated tube collectors, by

region, [21] ................................................................................................. 40 Figure 2.14 Main conversion routes for biomass to secondary energy

carriers [21] ................................................................................................. 41 Figure 2.15a Steam plant using a vapour or dry steam dominated

geothermal source [21] ............................................................................... 42 Figure 2.15b Single stage flash plant using a water dominated geothermal

resource separator to produce steam [21] ................................................... 42

XVIII

Figure 2.16 Cascading the use of geothermal resource for multiple

application [21] ........................................................................................... 43 Figure 3.1 Map of CONCERTO cities [54] ............................................... 48 Figure 3.2 Idea of CONCERTO project [56] ........................................... 49 Figure 3.3 Energy performance achieved after implementation of measures

(In a Childcare Facility in North Tipperary)............................................... 51 Figure 3.4 Total installed RES power in CONCERTO [54] ..................... 57 Figure 3.5 Total installed RES electricity power in CONCERTO [54] .... 57 Figure 3.6 Total installed RES heating power in CONCERTO [54]......... 58 Figure 3.7 Total installed RES cooling power in CONCERTO [54] ........ 58 Figure 3.8 Structure of CONCERTO premium Technical Monitoring

Database Indicators .................................................................................... 60 Figure 3.9 Examples of the flow of the Technical Indicators for Building

and ESU: the nomenclature refers to the one adopted in the CONCERTO

technical monitoring database. ................................................................... 69 Figure 3.10 Planimetry of the Arquata District ......................................... 71 Figure 3.11 ATC building facade .............................................................. 72 Figure 3.12 ATC PV system ...................................................................... 74 Figure 3.13 Social housing buildings ........................................................ 74 Figure 3.14 Social house building PV system ........................................... 76 Figure 3.15 Residential building’s PV yearly production residential

building ....................................................................................................... 76 Figure 3.16 Arquata energetic and metering system [62] .......................... 77 Figure 3.17 Screen of CONCERTO Database........................................... 79 Figure 3.18 Final Energy Demand for CHP .............................................. 81 Figure 3.19 Final Energy Demand of Different ESU ................................ 82 Figure 4.1 Functional aspects of building automation systems (BAS) [66]

.................................................................................................................... 86 Figure 4.2 Configuration for a BAS [63] ................................................... 87 Figure 4.3 Calculation sequence of BACS efficiency factor method ........ 89 Figure 4.4 Synthetic scheme for heating/cooling system [68]................... 90 Figure 4.5 Heating curve [68] .................................................................... 92 Figure 4.6 Savings from occupancy based controls [75] ........................... 96 Figure 4.7 Savings from daylight linked controls [75] .............................. 98 Figure 4.8 Kindergarten’s plant ............................................................... 104 Figure 4.9 Recommended design values of the indoor temperature for

design of buildings and HVAC systems for Kindergarten (Norm UNI EN

ISO 15215)................................................................................................ 107 Figure 4.10 Illuminance for day-care and corridors from norm UNI EN

12464-1 ..................................................................................................... 107 Figure 4.11 Room’s position (the numbers are the same used in Table 4.9)

.................................................................................................................. 108 Figure 4.12 Control used for windows operation .................................... 109

XIX

Figure 4.13 Logic of the dimming control for lighting............................ 110 Figure 4.14 Case 1 monthly energy breakdown (energy use and need) .. 113 Figure 4.15 Annual energy breakdown for case 1 (primary energy) ....... 116 Figure 4.16 Annual Primary energy comparison between case 1 case 2

case 5 and case 6 ...................................................................................... 117 Figure 4.17 Change in reduction cooling energy need, changing the solar

set point (case 3) ....................................................................................... 118 Figure 4.18 Change in delta lighting and cooling primary energy changing

the solar set point (case 4) ........................................................................ 118 Figure 4.19 Reduction of the total primary energy consumption for case 4

and for case 8 changing the solar set point. .............................................. 119 Figure 4.20 Case 9 monthly energy breakdown (energy use and need) .. 120 Figure 4.21 Annual energy breakdown for case 9 (primary energy) ....... 122 Figure 4.22 Change in delta cooling energy need, changing the solar set

point (case 11)........................................................................................... 123 Figure 4.23 Total primary energy consumption of case 12 changing the

solar set point. ........................................................................................... 123 Figure 4.24a Collector plane orientation and optimisation for annual yield

.................................................................................................................. 127 Figure 4.24b Collector plane orientation and optimisation for winter

period ........................................................................................................ 127 Figure 4.25 Monthly comparison between primary energy used by the

building and primary energy generated by the PV (case 9 and ZEB on

annual basis) ............................................................................................. 128 Figure 4.26 Monthly comparison between primary energy used by the

building and primary energy generated by the PV (case 9 and ZEB on

monthly basis) ........................................................................................... 130 Figure AI.1 Case 1 monthly energy breakdown (energy use and need).. 135 Figure AI.2 Case 2 monthly energy breakdown (energy use and need).. 136 Figure AI.3 Case 5 monthly energy breakdown (energy use and need).. 140 Figure AI.4 Case 6 monthly energy breakdown (energy use and need).. 141 Figure AI.5 Case 9 monthly energy breakdown (energy use and need).. 145 Figure AI.6 Case 10 monthly energy breakdown (energy use and need) 146

XX

1 Smart City

1

1 Smart City

1.1 Introduction

Cities occupy approximately only 2% of earth ground but 55% of the

world population already live in towns and according to several institutions

in the near future this percentage will grow up to 70% by 2050 (In Europe

and urban population will exceed 80%, see Figure 1.1)

The rapid growth in population creates new problems for city services and

infrastructures, which include: difficulty in waste management, scarcity of

resources, air pollution, traffic congestions, inadequate and deteriorating

infrastructure, energy shortages and price instability, human health

concerns, demand for better economic opportunities [3,4]. The challenge of

contemporary cities is to deal with these issues in a sustainable manner and

at the same time to create new economic opportunities and social benefits

for everybody.

Because of this quick urbanization, cities will become increasingly

important for climate change mitigation. During the next 20-25 years the

cities’ share of energy demand and carbon emissions will approach the

80%-mark (see Figure 1.2). Within the EU, cities are responsible for about

70% of the overall primary energy consumption and this share is expected

to increase to 75% by 2030 [2]. Designing clean and sustainable energy

Figure 1.1 Percentage of EU population living in urban areas, 1950-2050

[1].

1 Smart City

2

solutions for cities is therefore of primary importance. In this context

energy technologies such as solar and wind power, or distributed

renewable power technologies in general, could well fit into city-scale and

provide the basis for a low carbon society [5].

Urbanization creates also more commuters and traffic. Traffic congestion

cost the U.S economy 78 billion dollars in 2005, resulting in 4,2 billion lost

hours, as well as pollution and wasted fuel, and these costs are growing at

8% per annum [6].

Cities also account for 60% of all water allocated for domestic human use,

while human demand for water is expected to increase six times in the next

50 years and some municipalities lose up to 50% of precious water through

leaky infrastructure [7]. Currently 2,8 billion people, live in areas of high

water stress. Present trends suggest that this will rise to almost 4 billion by

2030 [6].

In this context, a debate has emerged on the way new technology-based

solutions, as well as new approaches to urban planning and living, can

assure the future viability and prosperity in metropolitan areas. In this

discussion, the concept of Smart City (SC) has been the subject of

increasing attention and it now appears as a new paradigm of intelligent

and urban development and sustainable socio-economic growth.

Although there is not yet a general consensus on the meaning of Smart

Cities, there is an agreement about the fact that Smart Cities are

characterised by an intense use of Information and Communication

Technologies (ICT), which in various urban domains, help cities making

better use of their resources. Nevertheless ICT-based solution can be

14858 24880

36449 6368

6220

9112

0

10000

20000

30000

40000

50000

1990 2010 forecast 2030 forecast

CO

2 e

mis

sio

ns

(met

ric

ton

s)

Urban Non-urban

Figure 1.2 Global CO2 emissions (metric tons), 1990,2010 and 2030,

urban/non-urban Source U.S Energy Information Administration Annual

Outlook 2008.

1 Smart City

3

considered as just one of the various input for projects and ways to build

the SC, in fact a city equipped with ICT systems is not necessarily a Smart

City. Also the role of human capital/education, social and relational capital,

energy aspects (such as energy production, distribution and use), and

environmental issues are considered important drivers of urban growth

[3,8,9,10].

IBM provides a summary table for its vision of a smarter city (see Table

1.1) including some started initiatives.

Although useful to describe how leading ICT company foresee the future

of a Smart City, the vision of IBM, presents the energy aspect of the Smart

City in an approximate manner. The use of ICT technology to send price

signals and energy signals is, in fact, only one of the first steps to make a

city a Smart City from the energy point of view. In Chapter 2 of the present

thesis, we focus on Smart Buildings and on the energy aspects linked to

them.

Table 1.1 IBM vision of smarter cities [6].

Today What if a city could Already, cities are? People

Cities have difficulty

using all the

information at their

disposal.

Citizens face limited

access to information

about their healthcare,

education and housing

needs.

Reduce crime and react

faster to public safety

threats, by analysing

information in real-

time?

Use better connections

and advanced analytics

to interpret vast

amounts of data

collected to improve

health outcomes?

Putting in place a new

public safety system in

Chicago. Allowing real

time video

surveillance.

Giving doctors in

Copenhagen instant

access to patients’

health records.

Transport

Transporting people

and goods is impeded

by congestion, wasted

hours and wasted fuel.

Eliminate congestion

and generate

sustainable new

revenues, while

integrating all transport

modes with each other

and the wider

economy?

Bringing in a

dynamically priced

congestion charge for

cars to enter

Stockholm, reducing

emission by 14%.

Communication

Many cities have yet to

provide connectivity

for citizens

“Going online”

typically means at slow

speeds and at a fixed

Connect up all

businesses citizens and

systems with universal

affordable high-speed

connectivity?

Giving citizens and

business a range of

new services, from

automated recycling to

universal smartcards

for paying bills.

1 Smart City

4

location (Songdo, Korea) Water

Half of all water

generated is wasted,

while water quality is

uncertain

Analyse entire water

ecosystems from rivers

and reservoirs to the

pumps and pipes in our

homes?

Give individuals and

businesses timely

insight into their own

water use, raising

awareness, locating

inefficiencies and

decreasing unnecessary

demand?

Monitoring, managing

and forecasting water-

based challenges, in

Galway, Ireland,

through an advanced

sensor network and

real-time data analysis

Business

Businesses must deal

with unnecessary

administrative burdens

in some areas, while

regulation lags behind

in others.

Impose the highest

standards on business

activities, while

improving business

efficiency?

Boosting public sector

productivity while

simplifying processes

for business in Dubai

through a Single

Window System that

simplifies and

integrates procedures

across 100 public

services. Energy

Insecure and

unsustainable energy

sources

Allow consumers to

send price signals-and

energy- back to the

market, smoothing

consumption and

lowering usage?

Giving household

access to live energy

prices and adjust their

use accordingly, as in

Seattle, reducing stress

on the grid by up 15%

and energy bills by

10% on average.

1.2 Definitions

The idea of Smart City is still emerging and in the literature there are

various definitions of this concept [11,12]. The term is used all over the

world with different meanings; there is also a considerable overlap of the

Smart City concept with related city concepts such as Intelligent City,

Knowledge City, Sustainable City, Talented City, Wired City, Digital City

and Eco City [13].

Most of the definitions of the Smart City focus exclusively on the role of

ICT in linking city-wide services. Many Smart Cities are thus sophisticated

systems that “sense and act” [14] and in which a great volume of real-time

information is processed and integrated across multiple processes, systems,

organisations and value chains to optimise operations and inform

authorities on incipient problems. For example Forrester [4] defines the

1 Smart City

5

Smart City as: “the use of Smart Computing technologies to make the

critical infrastructure components and services of a city - which include

city administration, education, healthcare, public safety, real estate,

transportation, and utilities - more intelligent, interconnected and efficient

”. Smart Computing are defined also by Forrester as: “a new generation of

integrated hardware, software, and network technologies that provide IT

systems with real-time awareness for the real world and advanced analytics

to help people make more intelligent decisions about alternatives and

actions that will optimize business process and business balance sheet

results”. Therefore according to Forrester what makes a city a Smart City is

the use of Smart Computing to deliver its core services to the public in a

remarkably efficient manner.

Another definition of the Smart City based on ICT is given by IBM [8]: “

a instrumented, interconnected, and intelligent city. Instrumentation

enables the capture and integration of live real-world data through the use

of sensors, kiosks, meters, personal devices, appliances, cameras, smart

phones, implanted medical devices, the web and other similar data–

acquisition systems, including social networks as networks of human

sensors. Interconnection means the integration of those data into an

enterprise computing platform and the communication of such information

among the various city services. Intelligent refers to the inclusion of

complex analytics, modelling, optimization, and visualization in the

operational business processes to make better operational decisions”.

Other definitions, while retaining ICT’s important role, provide a broader

perspective, such as the following definition from Toppeda [7]: “a city

combining ICT and web 2.0 technology with other organizational, design

and planning efforts to dematerialize and speed up bureaucratic processes

and help to identify new, innovative solutions to city management

complexity, in order to improve sustainability and liveability”. Manville et

al. [13] say instead that “a city may be called smart when investments in

human and social capital and traditional and modern communication

infrastructure, fuel sustainable economic growth and a high quality of life,

with a wise management of natural resources through participatory

governance”.

The availability and quality of the ICT infrastructure is not the only aspect

of a Smart City, some studies [8-10][15] focus on the role of human

capital, culture and education in urban development, in particular Nam and

Pardo [8] say: “a city that gives inspiration, shares culture, knowledge and

life, a city that motivates its inhabitants to create and flourish in their own

lives”. Also Manville et al. [13] say “any adequate model for the Smart

City must therefore also focus on the Smartness if its citizens and

communities and on their well-being and quality of life, as well as

1 Smart City

6

encourage the processes that make cities important to people and which

might well sustain very different activities”.

To obtain a more complete definition for Smart City, we must think that no

system operates in isolation. A smarter city infuses information into its

physical infrastructure to improve conveniences, facilitate mobility, add

efficiencies, reduce energy waste, improve the quality of air and water,

identify problems and fix them quickly, recover rapidly from disaster,

collect data to make better decisions, deploy resources effectively and

share data to enable collaboration across all its stakeholders. However,

infusing intelligence into each subsystem is not enough to become a

smarter city. The city should be treated as an organic whole- as a network,

as a linked system [8]. Also Mayer et al. [14] says that “a city becomes a

Smart City after investment in human and social capital, sustainable

transport and modern ICT infrastructure, fuel sustainability, economic

development, and improvements in the quality life of its citizens. To cover

all these dimensions, natural resources (including energy) must be wisely

managed, and this management must be provided by the governments,

universities, renovated business models and citizens”. One of the latest

definition provided by UE [13] says “A smart city is a city seeking to

address public issues via ICT-based solutions on the basis of multi-

stakeholder, municipally based partnership”.

To sum up, there are a several activities which are described in the

literature concerning the Smart City, from ICT to economy, culture and

environment. A recurring structure for the Smart City based on six pillars

have been identified; the pillars are:

Smart Economy

Smart People

Smart Governance

Smart Mobility

Smart Environment

Smart Living

These six pillars connect with traditional regional and neoclassical theories

of urban growth and development.

From these six features, another definition can be derived: “a Smart City is

a city well performing in a forward-looking way in this six characteristics,

built on the smart combination of endowments and activities of self-

decisive, independent and aware citizens” [16].

1 Smart City

7

1.3 Areas of interest

In this chapter the six pillars of the Smart City are analysed: Smart

Economy, Smart People, Smart Governance, Smart Mobility, Smart

Environment and Smart Living.

By Smart Economy we mean e-business and e-commerce, increased

productivity, ICT-enabled and advanced manufacturing and delivery of

services, ICT-enabled innovation, as well as new products, new services

and business models. It also establishes smart clusters and eco-systems

(e.g. digital business and entrepreneurship). Smart Economy also entails

local and global inter-connectedness with physical and virtual flows of

goods services and knowledge.

Smart People is a concept that include more informed and educated and

participatory citizens, within an inclusive society that improves creativity

and fosters innovation. It can also enable people to themselves input, use,

manipulate and personalise data, for example through appropriate data

analytic tools and dashboards, to make decisions and create products and

services. It is critical also not to refer to members of the city only as

individuals, but also as communities and groups and their respective wants

and needs within cities. Some initiatives for Smart People are: assisted

permanent education (also on-line education), e-books loan, support forum

and expert advice in collaboration with the third sector, information on

trends in employment opportunities, mobility assistance and prevention of

social isolation for elderly disabled and chronically illnesses.

Smart Governance means joining up within-city and across-city

governance, integrate public, private, civil European Community

organisations so the city can function efficiently and effectively as one

organism, becoming more transparent and accountable, and giving citizens

access to information about decisions that affect their lives. Initiatives for

Smart Governance include: information sharing platforms based on cloud

computing for solving cross-cutting issues and lower bureaucracy, systems

of direct and secure access by internet to local information and public

services, de-materialization of bureaucracy by privacy and legal validity of

e-documents, collaborative discussion groups through which have direct

communication with public institutions, cultural sector and third sector.

Smart Mobility means ICT supported and integrated transport and logistics

systems. For example, sustainable, safe and interconnected transportation

systems can encompass trams, buses, trains, metros, cars, cycles and

pedestrians in situations using one or more modes of transport. Enhanced

travellers information services searching, by mobile devices, for stops

destinations and estimated arrival time of public transport. Detection and

analysis of traffic flows and intelligent management of signage giving

1 Smart City

8

priority to emergency Smart Mobility prioritises clean and often non-

motorised option in order to reduce energy consume and greenhouse gasses

emissions.

A large part of the Smart Environment concept pivoting around energy, in

particular, distributed generation from renewable sources, ICT-enabled

energy grids, metering, energy waste reduction, pollution control and

monitoring, renovation of buildings, green buildings, smart building

including home energy monitoring systems and home automation. The

Smart Environment includes also urban services such as street lighting and

waste management, drainage systems, water resource systems, and

transparent structures for monitoring and forecasting the quality of water,

noise and electromagnetic pollution

Smart Living means ICT-enabled life styles, behaviour and consumption.

Smart Living is also healthy and safe living in a city with different cultural

facilities and incorporates good quality housing and accommodation.

Smart Living is also linked to high levels of social cohesion and social

capital.

Gliffinger et al. [16] presents a table with 33 factors that describe the six

pillars (see Table 1.2). Smart Economy includes factors all around

economic competitiveness as innovation, entrepreneurship, productivity

and flexibility of the labour market. Smart People is not only described by

the level of qualification or education of the citizens but also by the quality

of social interactions regarding integration and public life. Smart

Governance comprises aspects of political participation, services for

citizens as well as the functioning of the administration. Local and

international accessibility are important aspects of Smart Mobility as well

as the availability of information and communication technologies and

modern and sustainable transport systems. Smart Environment is described

by attractive natural conditions, pollution etc. Finally Smart Living

comprises various aspects of quality of life as culture, health, safety and

house.

These Characteristics are also available on http://www.smart-

cities.eu/model.html, where they are used for benchmarking 70 Smart

Cities around Europe.

Table 1.2 Characteristics and factors of the Smart City [16]

Pillars Factors

Smart Economy

(Competitiveness)

Innovation Spirit – Entrepreneurship – Productivity -

Economic image & trademarks – Flexibility of labour market –

International appeal – Ability to transform

Smart People

(Social and Human

Capital)

Level of qualification – Affinity to lifelong learning –

Social and ethnic plurality – Flexibility – Creativity –

Cosmopolitanism - Participation in public life

Smart Governance Participation in decision-making – Public and social services –

1 Smart City

9

(Participation) Transparent governance – Political strategies & perspectives

Smart Mobility

(Transport and ICT)

Local accessibility – (Inter)national accessibility –

Availability of ICT-infrastructure – Sustainable innovative

transport systems

Smart Environment

(Natural resources)

Pollution – Environmental protection – Sustainable resource

management – Attractive natural conditions

Smart Living

(Quality of life)

Cultural facilities – Health conditions – Individual safety –

Housing quality – Education facilities – Touristic attractive –

Social cohesion

Lombardi et al. [9] proposes a small change to the six pillars, for them, in

fact, Smart Mobility is coincident with Smart Environment and they add

the pillar of Smart Human Capital. They also propose a modified triple

helix model to analyse the pillars. The triple helix model has recently

emerged as a reference framework for the analysis of knowledge-based

innovation systems, and relates the multiple and reciprocal relationships

between the three main agencies in the process of knowledge creation and

capitalization: universities, industry and government. To understand better

all Smart Cities facets the model is modified adding another unifying

factor to the analysis: civil society. In this model more factors for the six

pillars can be found too.

The way in which these pillars develop are very different and depending

on the starting idea of Smart Cities, culture and needs of region, political

priority and so on.

Margarita Angelidou [17] presented four different strategic choice for the

development a Smart City: national versus local strategies, for new versus

existing cities, hard versus soft infrastructure-oriented strategies, and

sector-based versus geographically-based strategies. The study presents

advantages and disadvantages of each choice and illustrates Smart City

strategy cases from all over the world. For example Smart Governance e

Smart Economy projects may be more likely to be pursued at a national

level; the associated issues may be harder to frame as “municipal

problems”. Cases of Smart Cities initiatives at a national level include

Italy’s project “Burocrazia! Diamoci un taglio!”

(http://www.magellanopa.it) a national initiative aimed at encouraging

citizens to use digital tools.

Forrester [4] deviates from the six pillars and provides instead seven

critical infrastructure components and services that a Smart Cities must

develop: city administration, education, healthcare, public safety, real

estate, transportation, and utilities. Figure 1.3 presents a visions of the

Smart City for Forrester, and of the seven infrastructure components:

City Administration: Streamline management. In United states, for

example, President Barack Obama is pushing for “Open

Government”. This initiative focuses on using ICT to make

1 Smart City

10

political decisions transparent to citizens. Another example of this

model is South Korea’s new city, Songdo International Business

District which promotes free economic zones to foster business and

employment growth.

Education: Increase access, improve quality, and reduce costs. The

heightened use of technology in education will increase access,

improve the quality and experience, and reduce costs.

Healthcare: Increase the availability and provide more rapid,

accurate diagnosis. A smart healthcare system is built on scalable

storage systems and communications platform. Patient records are

electronically stored and shared wherever they are needed. The

communication platform enables quick response to emergency

services.

Public Safety: Use real-time information to respond rapidly to

emergencies and threats. With more people living in the city,

police, fire and other public safety personnel need to respond more

quickly to emergency situations. Smart public safety initiatives

around the world are experimenting with communication

technologies to feed real-time information to fire and police

departments.

Real estate: Reduce operating costs, increase the value, and

improve occupancy rates. Smart real estate delivers a lot of

financial and environmental benefits. Using of Smart Computing

technologies such as building management systems to automate

heating and cooling and sensors to power down lights when not in

use. With these operations buildings can reduce energy

consumption, maintenance costs and greenhouse gas emissions.

Transportation; Reduce traffic congestion while encouraging the

use of public transportation. Offering faster and more convenient

public transportation alternatives is already on most cities’ road

maps to reduce congestion and related financial and environmental

impacts.

Utilities: Deliver only as much energy or water as in required while

reducing waste. A smart utility infrastructure for energy and water

entails making existing systems efficient and finding new ways of

producing and delivering water, gas, and electricity. Cities also are

implementing Smart Grids in such a way citizens and businesses

can look at energy consumptions and manage use accordingly.

They are also planning to replace carbon-intensive fuel with

renewable energy.

1 Smart City

11

Figure 1.3 Smart City blueprint [4].

Another way to analyse the areas of interest of the Smart Cities is

described by Neirotti et al. [14]. They studied two different approaches; the

one sees cities as factories for life, on the basis of broad use of ICT that

enables central planning and an integrated view of the processes that

characterise urban operations. The emphasis of this approach is on

production and distribution of energy, transportation and logistics, waste

management and pollution control, and it looks at the way ICT can harness

information processing in these fields, these are called “hard” domains.

The other position instead views the ways of buildings SCs as being based

more on bottom-up approaches in which cities provide access to data and

allow citizens to make their own decisions. Consequently it stresses the

importance of investments in “soft” urban living domains. ICT plays a

more limited role in enabling sustainability and handling “transactions”,

which is thus related to welfare and social inclusion polices, culture and

1 Smart City

12

education. The “hard” and “soft” domains, their respectively sub domain

and a short description are available in Table 1.3

Table 1.3 “hard” and “soft” domains and their respectively sub-domain and a short

description [14].

Domain Sub-Domain Description

Natural resources

and energy “hard”

Smart grids

Public lightening

Green/renewable

energies

Waste management

Water management

Food and

agriculture

Electricity networks able to take into

account the behaviours of all the connected

users in order to efficiently deliver

sustainable, economic, and secure

electricity supplies. Smart Grids should be

self-healing and resilient to system

anomalies.

Centralised management systems that

directly communicate with the lampposts

can allow reducing maintenance and

operating costs, analysing real-time

information about weather conditions and

consequently regulating the intensity of

light by means of LED technology.

Exploiting natural resources that are

regenerative or inexhaustible, such as

heath, water, and wind power.

Collecting, recycling, and disposing waste

in ways that prevent the negative effects on

both people and the environment.

Analysing and managing the quantity and

quality of water throughout the phases of

the hydrological cycle and in particular

when water is used for agricultural

municipal and industrial purposes.

Wireless sensor networks to manage crop

cultivation and know the conditions in

which plants are growing

Transport and

mobility “hard”

City logistics

Info-mobility

People mobility

Improving logistics flows in city by

effectively integrating business need with

traffic conditions and environmental issues.

Distributing and using selected dynamic

and multi- modal information, both pre-trip

and, more importantly on-trip, with the aim

of improving traffic and transport

efficiency as well as assuring a high quality

travel experience .

Innovative and sustainable ways to provide

the transport of people in cities, such as the

development of public transport modes

base on environmental-friendly fuels.

Buildings “hard” Facility

management

Cleaning maintenance, property, leasing,

technology and operating modes associated

with facilities in urban areas.

1 Smart City

13

Building services

Housing quality

Various systems existing in a building such

as electric networks, fire safety,

telecommunication, and water supply

systems.

Aspects related to the quality of life in a

residential building such as comfort,

lighting, and Heating, Ventilation and Air

Conditioning (HVAC). It includes all that

concerns the level of satisfaction of people

living in a house.

Living “soft” Entertainment

Hospitality

Pollution control

Public safety

Healthcare

Welfare and social

inclusion

Culture

Public spaces

management

Ways of stimulating tourism and providing

information about entertainment events and

proposals for free time.

Ability of a city to accommodate foreign

students and tourists.

Controlling emissions and effluents by

using different kinds of devices.

Stimulating decisions to improve the

quality of air, water, and the environment in

general.

Collecting and monitoring information for

crime prevention.

Prevention, diagnosis, and treatment of

disease supported by ICT.

Improving the quality of life by stimulating

social learning and participation.

Facilitating the diffusion of information

about cultural activities.

Care, maintenance, and active management

of public spaces to improve the

attractiveness of a city.

Government “soft” E-government

E-democracy

Transparency

Digitizing the public administration by

managing documents and procedures

through ICT tools in order to optimise

work.

Using innovative ICT systems to support

ballots.

Enabling every citizen to access official

documents in a simple way and to take part

in the decision processes of a municipality

Economy and

people “soft”

Innovation

Cultural heritage

management

Digital Education

Human capital

management

Measures to foster the innovation systems

and in the urban ecosystem.

The use of ICT systems for delivering new

customer experience in enjoying the city’s

cultural heritage.

Extensive Use of modern ICT tools in

public schools.

Policies to improve human capital

investments and attract and retain new

talents.

1 Smart City

14

This brief literature analysis showed that the energy aspects of the Smart

Cities are often faced approximately and not plainly. Furthermore energy

aspects are never rigorously included in the definitions of Smart Cities

neither as a fundamental pillar. However energy is clearly fundamental for

the Smart City concept because it is interconnected with all of the pillars.

Research projects on the Smart City concerning energy aspects are, in fact,

the most developed, as demonstrated by Neirotti et al. [14].

Their study focuses on 70 cities all around the world and discovered that

about two thirds of the sample reports the development of projects in the

field of renewable energies and half on the sample refers to mobility

systems, also smart grids, water management and housing quality have

important roles (Figure 1.4). On the other sides projects focused on

governance and people are show little diffusion.

Smart City initiatives can be considered a useful vehicle for cities to

achieve their Europe 2020 targets, listed in Table 1.4. Cities are

conurbations that house a significant number of people, often in densely

populated areas. Therefore, cities as Smart entities may be particularly well

suited to initiatives addressing local public goofs problems, such as energy

and climate change [13]. Moreover, the impacts may be highly visible,

especially compared with less densely populated areas.

Eduardo de Olivera Fernandes [2] explains that the role of cities in

achieving these EU energy policy targets for 2020 follows from three

issues:

1. The need for collective action, about 80% of European citizens live

and work in a city, and also the most energy-intensive activities

Figure 1.4 Percentage of development of Smart City project [14].

1 Smart City

15

(e.g. industries), are located in cities [2]. A global solution for

climate change, even if achievable, would rely on the participation

of these citizens so that it is essential to have policies at multiple

levels, especially at city level.

2. The relevance of an energy demand side approach, with such an

approach, the matching of energy needs and energy options for

supply at the local level can leverage the overall energy efficiency

of all energy systems lowering the pressure on energy resources

bringing local environment benefits.

3. The innovation in sustainable technologies and measures,

hampered by a combination of market and institutional failures.

Recent theory emphasize that innovation is a process where

technology and institutions co-evolve accumulating learning

effects. In this respect, the role of city authorities is twofold as they

are both local energy policy makers that can be subject to

institutional failures and energy actors that can be subject to market

failures. Table 1.4 Europe 2020 targets for the EU as a whole [13].

Focus area Targets

Employment 75% of 20-64 years olds to be employed.

R&D and innovation 3% of EU’s GDP (public and private combined) to be invested in

R&D or innovation.

Climate change and

energy

Greenhouse gas emissions to be 20% (or even 30%, if the

conditions are right) lower than 1990.

20% of energy from renewables.

20% increase in energy efficiency.

Education Reduce school drop-out rates below 10%.

At least 40% of 30-34 years olds have completed third level

education.

Poverty and social

exclusion

At least 20 million less people in or at risk of poverty and social

exclusion.

Manville et al. [13], studying the Smart City initiatives aligned with

Europe 2020 target, observed that activities about energy and environment

are developed in over 50% Smart Cities project all around Europe (Table

1.5). This underlines again the central role that energy has in the Smart

Cities contest.

At least, the link between energy and the Smart City project can be

displayed by Figure 1.5, which shows the “technology roadmap”, drawn

up by the European Community for the European Initiatives on Smart

Cities. The focus of this roadmap is on buildings, heating and cooling,

electricity and transport. In general, it concerns technologies that aim to

improve the environment and therefore does not include all aspects of the

Europe 2020 targets. However it usefully illustrates the potential for Smart

1 Smart City

16

City Initiatives to contribute toward some of the objective of Europe 2020

(http://setis.ec.europa.eu/set-plan-implementation/technology-roadmaps).

Table 1.5 The number of cities with initiatives directly or indirectly aligned with

Europe 2020 targets [13].

Europe 2020 targets Number of cities

Employment 4

R&D and innovation 2

Energy and environment 18

Education 1

Poverty and social exclusion 7

Figure 1.5 European initiative on the Smart Cities technology roadmap (Source

European Commission)

It is furthermore possible to notice that energy could be connected with all

of the six main pillars of the Smart City.

Energy is the main parameter in Smart Environment, in fact, energy

production is the primary cause of pollution and emission of greenhouse

gasses, In Europe the 80% of carbon emissions comes from urban areas

[18]. Moreover energy production currently accounts for between 30 and

1 Smart City

17

40 percent of all water withdrawals in the Organisation for Economic Co-

operation and Development (OECD) states [19].

Transportation weighs heavily on climate, energy security, and

environmental policies, as 95% of transport energy comes from oil based

fuels [20].

Regarding Smart Living and People energy activities affect human health

wellbeing, and they are strongly connected with buildings where people

live and work. Energy used for cooling, heating, is closely linked to

welfare of people that use a building. Health problems caused by airtight

buildings without adequate ventilation, the so-called sick building

syndrome, were first identified as a result of reducing air change and

infiltration rete as an energy conservation measure [21].

Energy is also one of the primary point in politic agenda; moreover urban

planning and its impact on the urban tissue is a key factor in the demand

for transport and consequently energy.

The municipalities are typically in charge of the buildings’ licensing. They

are at first instance responsible for checking if the new and retrofitted

buildings comply with international or local requirements, and in some

cases they may even require performance levels for new buildings stricter

than the national standards and create favourable conditions. Moreover,

city authorities are themselves energy users, through buildings and

municipal fleet ownership, public lighting, street semaphores,

Nevertheless, it is important to consider that city authorities have to act

within the boundaries of policies defined at higher levels.

Finally it must be remembered that the cost of energy, in particular

electricity and fuel, impact strongly on economic activities.

However, despite the centrality of energy within the Smart City concept,

and despite the growing number of dedicated research projects. The theme

of energy within the Smart City is not well structured, it is also not well

defined and its explicit and implicit aspects are not highlighted clearly.

Being energy aspects of the Smart City very broad and multifaceted in this

thesis we focalize on energy in building, and in particular on the effect that

building automation and automatic controls have on them.

18

2 Definitions and characteristics of high performance buildings

19

2 Definitions and characteristics of high

performance buildings

2.1 Buildings

Cities are the place where most energy services are needed and they are

therefore ultimately responsible for the use of energy resources. A city can

be seen as open system, where the energy as well as the other natural

resources is transformed to satisfy the needs of the different urban

activities [2]. In particular buildings cover a central and fundamental role

in city and in the Smart City.

Buildings systems providing thermal comfort, refrigeration, illumination,

communication and entertainment, sanitation and hygiene – are responsible

for a significant share of energy use worldwide. Buildings are responsible

for 40% of total European energy consumption and generate 36% of Green

House Gases (GHG) [19]. Figure 2.1 shows the perceptual building energy

consumption in different states.

Figure 2. 1 Building energy consumption in selected country [22].

In particular in Europe there are tons of old buildings that not only lack

energy efficiency, but also are a big source of pollution. Natalija Lepkova

et al. [23], show that over 50% of existing residential buildings in 25 EU

member states were built before 1970 and one third of dwellings were built

2 Definitions and characteristics of high performance buildings

20

between 1970 and 1990. Hence, structural member of more than half of the

buildings in Europe are old and energy loss can be prevented by their

renovation. For example Lithuania’s government approved the program of

buildings’ modernization. It states that 70% of the current housing stick

must be renovated by 2020.

The world Business Council for Sustainable Development (WBCSD) in

2009 conducted a research that found that the energy usage in buildings

could be cut dramatically providing a saving of as much as the entire

transport sector uses currently [20].

These study indicate the need to achieve energy-efficient buildings to

reduce their CO2 emissions and their energy consumption. Moreover the

building environment affects the quality of life and work of all citizens, in

fact approximately 90% of people spend most of their time in buildings.

Indoor comfort plays a significant role and poses a huge impact to preserve

inhabitant’s health, productivity and satisfaction [22].

Entering in the details of energy consumption in buildings, Rangan

Banerjee et al. [21] studied the breakdown of primary energy use in

commercial and residential buildings by end-use services in the United

States. Figure 2.2 demonstrates that five energy services accounted for

86% of primary energy use in buildings. These were: thermal comfort

(space conditioning that includes space heating, cooling and ventilation),

illumination, sanitation and hygiene, including water heating, washing and

drying clothes, and dishwashing, communication and entertainment, and

provision of food refrigeration and cooking.

Figure 2.2 Primary energy use in United States commercial and residential buildings

in 2010 [21].

2 Definitions and characteristics of high performance buildings

21

The problem of high energy consumption in building, in not be faced

similarly in the world, in fact, since the climatic factors like temperature,

humidity, solar irradiance etc. differ from place to place, resulting in

different overall and energetic choice for both residential and commercial

building as: shape, materials typology, thermal insulation, use of solar

shading, use of transparent components etc.

Therefore is natural that the optimal solutions are not the same all across

the world. Also, some social characteristics, such as the preference for

detached/ single family vs. multi-family buildings, family size, average

income, and dwellings’ sizes, have an important influence on building

energy demand [2].

Maria V. Moreno et al. [20], identify different challenges, for reach the

perfect building for the Smart Cities, in the building value chain (from

design to end of-life of buildings), which can be summarized as follows:

Design: The design of buildings should be integrated, holistic, and

multi-targeted.

Structure: The structure of buildings should provide features such

as safety, sustainability, adaptability and affordability.

Building envelope: This should ensure efficient energy and

environmental performance

Energy equipment and systems: Advanced heating/cooling and

domestic got water solutions, including renewable energy sources,

should focus on sustainable generation as well as on heat recovery.

Among these systems, thermal storage in recognized as a major

breakthrough in building design. Distributed/decentralized energy

generation should address the key requirement of finding smart

solutions for grid-system interactions on a large scale.

Construction processes: These should consider ICT-aided

construction, improving the energy performance delivered and

using automated construction tools.

Performance monitoring and management: This should ensure

interoperability among the different subsystems of the building,

including smart energy management systems that provide flexible

actions to reduce the gap between predicted and actual energy

building performance, occupancy modelling, the fast and

reproducible assessment of designed or actual performance, and

continuous monitoring and control during service life.

End of life: This should include decision-support concerning

possible renovation or the construction of a new building and

associated systems.

2 Definitions and characteristics of high performance buildings

22

The high performance buildings are trying to face those challenges. In this

chapter we present the definition and characteristics of these high

performance buildings in particular passive building, Zero Energy Building

(ZEB) and Smart Building.

2.1.1 Passive building

The expression passive buildings refers to a buildings that use external

climatic factors, to heat, cool or light a buildings. Passive solar or passive

cooling designs take advantage of the sun’s energy to maximize heating or

cooling based on a building’s sun exposure. Systems that employ passive

design require very little maintenance and reduce a building’s energy

consumption by minimizing or eliminating mechanical systems used to

regulate indoor temperature and lighting [24].

The passive building approach can include the structure of the building

itself, including building orientation, window placement, skylight

installation, insulation and building materials, or specific elements of a

building, such as windows and window shades.

The basic idea of the passive buildings concept, explained by Feist at al.

[25], is to improve the thermal performance of the envelope to a level that

the heating system can be kept very simple. Two criteria are to be

considered: thermal comfort requirements in regard to radiation asymmetry

and the space heating load. First, the heat distribution system can be

simplified, if the surface temperatures of outer walls and windows are

close enough to the room air temperature. This allows for thermal comfort

without the need to place radiators at outer walls. Second, if the space heat

demand is low enough, space heating can be provided by the ventilation

system alone, at hygienic flow rates, without the need for recirculation or

for any additional water based heat distribution system. This allows for

very simple and cost effective air heating systems.

The story of passive buildings starts when George Frederick Keck, an

American architect, became a pioneer in the design of passive solar houses

after the demonstration of his all-glass “House of Tomorrow” at the

Chicago Century of Progress Expo in 1933 [26].

Since the late 1980s some notable schemes and standards for low-energy

buildings were developed. Amongst the most popular European low energy

buildings standards are the German Passivhaus (http://passiv.de/en/), the

French “LowEnergy Concumption Building” Bàtiment Basse

Consommation (BBC) and the italian Casaclima

(http://www.agenziacasaclima.it/it/casaclima/1-0.html) [26].

The Passivhaus standard is considered as the most internationally influent

standard with at least 25000 certified projects in Europe. It has been

developed through a series of projects by Professor Wolfgang Feist of the

2 Definitions and characteristics of high performance buildings

23

Institute for Housing and the Environment of the Darmstadt University in

Germany. Characteristics of Passivhaus are available on

(http://www.passipedia.org/start) and are:

Passivhaus allow for space heating and cooling related energy

saving of up to 90% (compared with typical building stock and

over 75% compared to average new builds. Passivhaus use less

than 1,5 l of oil or 1,5 𝑚3 of natural gas to heat one square meter of

living space for a year, substantially less than common “low

energy” buildings. Vast energy savings have been demonstrated in

warm climates where typical buildings also require active cooling.

Passive Houses make efficient use of the sun, internal heat sources

and heat recovery, rendering conventional heating systems

unnecessary throughout even the coldest of winters. During warmer

months, Passive Houses make use of passive cooling techniques

such as strategic shading to keep comfortably cool.

Passive Houses are praised for the high level of comfort they offer.

Internal surface temperatures vary little from indoor air

temperatures, even in the face of extreme outdoor temperatures.

Special windows and a building envelope consisting of a highly

insulated roof and floor slab as well as highly insulated exterior

walls keep the desired warmth in the house – or undesirable heat

out.

A ventilation system imperceptibly supplies constant fresh air,

making for superior air quality without unpleasant draughts. A

highly efficient heat recovery unit allows for the heat contained in

the exhaust air to be re-used.

Key requirements for a passive house are [25]:

The Space Heating Energy Demand is not to exceed 15 kWh per

square meter of net living space (treated floor area) per year or 10

W per square meter peak demand. In climates where active cooling

is needed, the Space Cooling Energy Demand requirement roughly

matches the heat demand requirements above, with a slight

additional allowance for dehumidification.

The Primary Energy Demand, the total energy to be used for all

domestic applications (heating, hot water and domestic electricity)

must not exceed 120 kWh per square meter of treated floor area per

year.

In terms of Airtightness, a maximum of 0.6 air changes per hour at

50 Pa pressure (ACH50), as verified with an onsite pressure test (in

both pressurized and depressurized states).

2 Definitions and characteristics of high performance buildings

24

Thermal comfort must be met for all living areas during winter as

well as in summer, with not more than 10 % of the hours in a given

year over 25 °C. For a complete overview of general quality

requirements (soft criteria).

All of the above criteria are achieved through intelligent design and

implementation of the 5 Passivhaus principles: thermal bridge free design,

superior windows, ventilation with heat recovery, quality insulation and

airtight construction (http://passiv.de/en/), see (Figure 2.3)

Figure 2.3 Illustration of the 5 Passivhaus principles.

Thermal insulation

All opaque building components of the exterior envelope of the house must

be very well-insulated. For most cool-temperate climates, this means a heat

transfer coefficient (U-value) of 0.15 W/(m²K) at the most, i.e. a maximum

of 0.15 watts per degree of temperature difference and per square metre of

exterior surface are lost.

Passive House windows

The window frames must be well insulated and fitted with low-e glazings

filled with argon or krypton to prevent heat transfer. For most cool

temperate climates, this means a U-value of 0.80 W/(m²K) or less, with g-

values around 50% (g-value = total solar transmittance, proportion of the

solar energy available for the room).

Ventilation heat recovery

2 Definitions and characteristics of high performance buildings

25

Efficient heat recovery ventilation is key, allowing for a good indoor air

quality and saving energy. In Passivhaus, at least 75% of the heat from the

exhaust air is transferred to the fresh air again by means of a heat

exchanger.

Airtightness of the building

Uncontrolled leakage through gaps must be smaller than 0.6 of the total

house volume per hour during a pressure test at 50 Pa (both pressurised

and depressurised).

Absence of thermal bridges

All edges, corners, connections and penetrations must be planned and

executed with great care, so that thermal bridges can be avoided. Thermal

bridges which cannot be avoided must be minimised as far as possible.

Besides the definition of the global requirements to comply with the

Passivhaus standard, recommendations for component quality and planning

and construction methods are given [24]. Examples are listed in Table 2.1.

Table 2.1 Examples of component quality and construction suitable to reach the

Passivhaus standard (“recommended”) and best available components (“best

practice”) [25].

Component or construction Recommended Best Practice

Insulation of opaque envelope, U (W/(𝑚2𝐾)) <0,15 0,06

Thermal bridge free construction, i.e.

Linear thermal transmittance, 𝜓𝑒 (W/m K))

<0,01 <0

Glazing with low U-value and high g-value, i.e.

Thermal transmittance, 𝑈𝑒 (W/(𝑚2𝐾))

Total solar energy transmittance, g (%)

<0,8

>50

0,51

58

Window, thermal bridge free construction, insulated

frame, , 𝑈𝑤 (W/(𝑚2𝐾))

<0,8

0,75

Heath recovery with

Net efficiency, 𝜂𝐻𝐸 (%)

Heath loss through casing

Internal and external leakages (%)

>75

<5 W/K

<3

92

<1

Electric energy demand for ventilation including

control 𝜌𝑒𝑙 (W/(𝑚3𝐾))

<0,45

0,3

The technical, economic and social feasibility of the Passivhaus concept

has been proven with the European project “ Cost Efficient Passive Houses

as European Standards “ (CEPHEUS) 221 housing units complying with

the Passivhaus standard were built in five European countries and their

operation was evaluated. The aim was to demonstrate the technical

feasibility at low extra cost for a variety of different buildings, construction

and designs implemented [24] see Figure 2.4.

2 Definitions and characteristics of high performance buildings

26

Figure 2.4 Comparison of the measured energy consumption of all CHEPHEUS

project with the corresponding values of ordinary, newly erected buildings according

to present standards [25].

In France, the BBC-Effinergie incorporates the regulatory requirements for

the energy performance of buildings. The BBC-Effinergie label can be

acquired by buildings, whose primary energy requirements for heating,

cooling, ventilation, hot water, and lighting do not exceed the 50

kWh/m2/year (http://www.effinergie.org/index.php/les-labels-

effinergie/bbc-effinergie).

Instead, the Casaclima certification (http://www.agenziacasaclima.it/)

creates 3 different categories depending from the building consumption

(Figure 2.5)

Figure 2.5 Classes for Casaclima certification. (Casaclima oro, Casaclima A,

Casaclima B)

2 Definitions and characteristics of high performance buildings

27

2.1.2 Zero Energy Buildings (ZEB)

When we talk about buildings in the Smart City one of the topic that most

appear in the literature is the concept of Zero energy Building (ZEB). The

Zero Energy Building (ZEB) concept is no longer perceived as a concept

of a remote future, but as a realistic solution for the mitigation of 𝐶𝑂2

emissions and the reduction of energy use in the building sector [23][27-

29].

Goals for the implementations of ZEBs are discussed and proposed at the

international level e.g. in the USA within the Energy Independence and

Security Act of 2007 and at the European level within the recast of the

Directive on Energy Performance of Buildings (EPBD) adopted in May

2010 [30]. The EISA 2007 [30] authorizes the Net-Zero Energy

Commercial Building Initiative to support the goal of net zero energy for

all new commercial buildings by 2030. It further specifies a zero energy

target for 50% of U.S. commercial buildings by 2040 and net zero for all

U.S. commercial buildings by 2050. The EPBD [31] establishes the

“nearly zero energy building” as the building target from 2018 for all

public owned or occupied by public authorities building and from 2020 for

all new buildings.

Despite the clear international goals and the international attention given to

the ZEB, there could be many different interpretations of ZEB in particular

Natalija Lepkova et al. [23] identify 4 different terms that have used across

Europe for defining ZEBs, they are:

“Net Zero Site Energy, a site ZEB produces at least as much energy

as it uses in a year, when accounted for at the site.”

“Net Zero Source Energy, a source ZEB produces at least as much

energy as it uses in a year, when accounted for at the source. Source

energy refers to the primary energy used to generate and deliver the

energy to the site. To calculate a building’s total source energy,

imported and exported energy is multiplied by the appropriate site-

to-source conversion multipliers.”

“Net Zero Energy Costs: in a cost ZEB, the amount of money the

utility pays the building owner for the energy the building exports

to the grid is at least equal to the amount the owner pays the utility

for the energy services and energy used over the year.”

“Net Zero Energy Emissions: a net-zero emissions building

produces at least as much emissions-free renewable energy as it

uses from emissions-producing energy sources.”

2 Definitions and characteristics of high performance buildings

28

There is also no agreement on the period of time in which calculate the

energy balance. For the energy use of a building a year is the most

accepted calculation period. Another opinion, not very popular within the

building community, is the sub-yearly balance, i.e. seasonal or monthly.

By using these balancing periods it is more difficult to achieve zero

balance than in the case of annual balance, since the seasonal discrepancy

between energy demand and renewable energy generation [30].

The definition of ZEB considered in this thesis is given by the Energy

Performance of Building Directive (EPBD) [31], where it is described as

follows: “Net zero energy building means a building where, as a result of

the very high level of energy efficiency of the building, the overall annual

primary energy consumption is equal to or less than the energy production

from renewable energy sources on site, nearly zero energy building means

a building that has a very high energy performance, the nearly zero or very

low amount of energy required should be covered to a very significant

extent by energy from renewable sources including energy from renewable

sources produced on-site or nearby”.

If a house produces more energy than it consumes it may be called a “plus

energy” house or prosumer.

So EPBD states that a very high energy performance building can be

considered as a ZEB if it meets the following two conditions; require a

very low amount of energy requirements by renewable energy sources,

produced on-site or nearby. Very low energy buildings can be achieved

through passive approach and the selection of energy efficiency building

technologies. The use of high efficiency HVAC, lighting equipment and

appliances, as well as an adequate control system, are effective ways to

reduce the energy consumption. However, the potential energy saving

through an optimized passive design, minimizing the heating and cooling

loads, is usually more influential than the use of innovative HVAC

solutions [24,29],[32-34]

To clarify the situation, Table 2.2 developed by Marszal et al. [13],

proposes a ranking of preferred application of renewable energy sources.

Table 2.2 ZEB renewable energy supply option hierarchy [29]

Option no. ZEB supply-side options Examples

0 Reduce site energy use

through low-energy building

technologies

Day lighting, high-efficiency

HVAC equipment, natural

ventilation, evaporative

cooling, etc.

On site supply options

1

Use renewable energy

sources available within the

building’s footprint

PV, solar hot water, and wind

located on the building.

2 Definitions and characteristics of high performance buildings

29

2 Use renewable energy

sources available at the site

PV, solar hot water, low

impact hydro and wind located

on site but not on the building

Offsite supply options

3

4

Use renewable energy

sources available offsite to

generate energy on site

Purchase offsite renewable

energy sources

Biomass, ethanol or biodiesel

that can be imported from off

site, or waste streams from

onsite processes that can be

used on site to generate

electricity and heat

Utility based wind, PV,

emissions credits, or other

“green” purchasing options.

Hydroelectric is sometimes

considered

Figure 2.6, instead, shows that the use of passive strategies is one of the

key actions to achieve any ZEB categories since be a “very low energy

consumption building” is the first requisite.

Figure 2.6 Diagram of the ZEB approach. Passive design strategies are an essential

aspect to reduce the amount of energy required by the building [24].

In the literature there are numerous examples of ZEB built starting from

passive design.

The Solar Decathlon Europe, presented by Edwin Rodriguez-Ubinas et al.

[27], is a contents where participating houses are challenged to reach the

level of zero energy buildings.

2 Definitions and characteristics of high performance buildings

30

The buildings are characterized by a good balance between passive

strategies (means to minimize the energy demand), and high efficiency

equipment (means of reduce the consumption) see Figure 2.7.

Another example is presented by Fabian Ochs et al. [33], the aim of their

project is to achieve a net zero energy balance for heating and domestic hot

water including auxiliary energies but excluding household electricity for

two multi-family houses in Innsbruck (Austria), starting from building

optimized to Passivhaus standard.

Stephen Berry et. al [29] illustrate the case study of Lochiel Park Green

Village in South Australia. Lochiel Park was created through government

policy to become a suburb of over 100 net zero energy homes. In this site

all homes incorporate passive solar design principles to decrease the need

for additional heating and cooling.

We must emphasize, however, that not all passive buildings are ZEB and

not all ZEB are built with passive techniques. Simply passive buildings

have more chance to become ZEBs due to their low consumptions, which

lead to a lower energy contribution from renewable sources. Alessandro

Gallo et. al [31] study different ZEBs in Spain, that not use passive

techniques.

To conclude ZEBs have been proven to act as key players in the

development of Smart Cities by Kylili and Fokaides [26], since they are

anticipated to contribute significantly on the energy aspect of the Smart

Cities, principally addressing challenges regarding the energy-efficiency,

renewable energy generation, and energy management.

ZEB influenced smart cities under these aspects:

- Environmental design and building practices,

- Renewable Energy Sources (RES),

- Labelling of technical building systems,

- Intelligent Energy Management.

2 Definitions and characteristics of high performance buildings

31

Figure 2.7 SDE 2012 houses passive strategies and other energy efficiency solutions

[27].

2 Definitions and characteristics of high performance buildings

32

2.1.3 Smart Buildings

Referring to the emerging information and communication technologies

(ICT), the concept of smart is receiving a great attention worldwide.

Although there is an increasing amount of academic and industrial

literature addressing Smart Buildings as a concept, there are few

definitions as to what they are. The definitions are also very different

between them [35] .

The definition of Smart Building provided by AmirHosein

GhaffarianHoseini et al. [36] says: “Smart Building are defined as

modernized sensor embedded residences with various integrated systems

that are capable of communicating each other while being controlled

remotely. A smart building promises to create better living environments”.

Clements-Croome [37] states that “A smart building can be described as

one that will provide for innovative and adaptable assemblies of

technologies in appropriate physical, environmental and organizational

setting, to enhance worker well-being, productivity, communication and

overall satisfaction”

Anna Kramers and Orjan Svane [38] define Smart Building as a term

employed for a suite of technologies that use ICT applications to make the

design, construction and the use of buildings more efficient and

convenient.

Smart Buildings LLC (a US-based engineering and design firm) offers this

definition: “A smart building is the integration of building, technology, and

energy systems. These systems may include building automation, life

safety, telecommunications, user systems and facility management

systems. Smart buildings recognise and reflect the technological

advancements and convergence of building systems, the common elements

of the systems and the additional functionality that integrated systems

provide. Smart buildings provide actionable information about a building

or space within a building to allow the building owner or occupant to

manage the building or space.”

Accenture describes its own smart-building solution as one that “leverages

an existing building’s systems information infrastructure to enable

operational savings through continuous, data-driven analytics and remote

implementation.” http://www.accenture.com/.

According to the European Commission, “Smart buildings means buildings

empowered by ICT (information and communication technologies) in the

context of the merging Ubiquitous Computing and the Internet of Things:

the generalisation in instrumenting buildings with sensors, actuators,

micro-chips, micro- and nano-embedded systems will allow to collect,

filter and produce more and more information locally, to be further

2 Definitions and characteristics of high performance buildings

33

consolidated and managed globally according to business functions and

services.”

From these definitions we can understand that a building is Smart when it

include a lot of ICT, and this ICT brings to better lifestyle, social

interaction and use of environments for the inhabitants. But this is an

incomplete concept, as one of the primary scope of ICT in buildings must

be reduce the energy consumption of the building. Therefore, we introduce

the following definitions.

The definition of Smart Building provided by IBM says: “Smarter

buildings are well managed, integrated physical and digital infrastructures

that provide optimal occupancy services in a reliable, cost effective, and

sustainable manner. Smarter buildings help their owners, operators and

facility managers improve asset reliability and performance that in turn

reduces energy use, optimises how space is used and minimises the

environmental impact of their buildings.” http://www.ibm.com/.

Siemens [39] says that, “only ICT solutions which create the greatest

synergies between energy efficiency, comfort and safety and security will

be sustainable over the long term, and these solutions could turn buildings

into living organisms: networked, intelligent, sensitive and adaptable; the

Smart Buildings”

Wang et al [40] suggest that Smart Building are part of the next generation

building industry, suggesting that they address both intelligence and

sustainability issues by utilising ICT technologies to achieve the optimal

combination of overall comfort level and energy consumption.

The Climate Group [41] describes the term Smart Building as a suite of

technologies used to make the design, construction and operation of

buildings more energetically efficient. These might include building

management systems (BMS) that run heating cooling and ventilation

systems according to occupants’ needs and software that switches off

lighting and electric appliances when they are not in use

Figure 2.8 shows in general as a Smart Building is made off.

In addition, we should remember, that increasing the technology in

buildings, but not rising users’ consciousness of the installed ICT

technology has no sense and indeed could be harmful.

The first step to create buildings that are high performance building must

be reducing the energy need, then the building could be optimized by the

use of ICT to reduce the energy consumption.

We could introduce the definition provided by Cinarelli et al. [42],

according to them, a Smart Building is:

A building that aims to reduce its environmental impact through the

reduction of energy consumption of the building/plants systems and

through an intelligent use of energy.

2 Definitions and characteristics of high performance buildings

34

A building equipped with ICT technologies able to allow a simple

management of electronic systems and able to increase efficiency

and environmental sustainability.

Figure 2.8 Smart energy building for Morvaj et al. [19]

Therefore, Information and Communication Technologies (ICT) could

play a role as a key enabler for decreasing energy usage in buildings. ICT-

based monitoring, feedback and optimisation tools can be used to reduce

both at every stage of a building’s life cycle, from design and construction

to use and demolition [41].

Shaik et al. [22] analyse the literature regarding the building consumption

in some selected countries, and calculate, through the study of different

works focused on intelligent control of energy and comfort management, in

the potential savings that could be achieved through the intelligent

automation in buildings (See Table 2.3).

Smart meter

It enables two-way communication and

remote reading. Building’s user has

insight in real time consumption and

price of the energy based on which

he/she can program response of the

building

Broadband connection

Enables communication with the grid

and other smart building via BPL,

WiMAX, GSM or other

communication standard

Building Automation

Consisting of sensors, actuators,

controllers, central unit, interface and a

network standard for communication. It

enables users to program building’s

behaviour based on defined condition.

Smart appliances

It can be part of the building

automation or by itself. It can monitor

building’s conditions and turn off or on

itself based on user’s defined

conditions.

2 Definitions and characteristics of high performance buildings

35

Table 2.3 Building energy consumption and GHG emission with saving potential in

selected countries and world [22]

Country/Region Building energy

consumption (%) 𝐂𝐎𝟐 emissions (%) Potential saving (%)

USA 40 40-48 20

EU 40-42 35-40 27-30

China 33 - -

Netherland 34 - -

Iran 35 - -

Turkey 36 32 30

Greece 30 40 -

Mexico 19 - -

UK 39 - -

Serbia 50 - 20

Singapore 53,2 21,4 -

Western countries 40 - -

Global 40 30 5-30

The Climate Group [41] presents the expected reduction in total emissions

of 𝐶𝑂2 that can be achieved by smart building (see Figure 2.9). Figure

2.10, always provided by The Climate Group [41], shows the expected

impact that each dimension could achieve within the reduction target.

As we could see from Figure 2.9, Building Automation system (BAS) is

one of the main important tools to reduce the emissions of CO2 in building.

Building automation system (BAS) is comprised of electronic equipment

that automatically performs specific facility functions. The commonly

accepted definition of BAS includes the comprehensive automatic control

of one or more major building system functions required in a facility such

as heating ventilating and air conditioning (HVAC) systems. In many cases

a BAS includes also, lighting, security, fire safety, and water management.

Where utilities offer incentives for demand response, the BAS allows the

operator to reduce energy consumption during peak summer/winter loads

by changing the system parameters such as raising/lowering the thermostat

temperatures, turning off sections of lights, HVAC, or other processes to

be operated during off-peak times.

Maria V. Moreno et al. [20] studied the energy consumption savings in

heating, obtained applying the BAS system called CityExplorer, in the

Technology Transfer Centre of the University of Murcia. The daily energy

saving values achieved during the month of operation of the energy

management system compared with the previous month varies between

14% and 30%.

2 Definitions and characteristics of high performance buildings

36

Figure 2.9 Expected reduction in total emissions of 𝐂𝐎𝟐 with ICT technologies [41]

Figure 2.10 Total ICT-enabled smart buildings abatement expanded [41]

More details on the energy effect of automatic control on buildings energy

need and use will be presented in chapter 4 of this thesis.

87%

13% Total emission frombuildings (includingpower)

Total ICT-enabled smartbuildings abatement

4%

27%

23% 14%

9%

8%

7% 1% 7%

Intelligent commissioning

improved building designfor energy efficiency

Building automationsystem (BAS)

Voltage optimisation

Benchmarking andbuilding recommissioning

Heating, ventilation andair conditioning (HVAC)

2 Definitions and characteristics of high performance buildings

37

2.2 Distributed energy production

Traditionally power plants are large centralised units, however, clean and

cost effective energy generation is a key issue in the Smart City, so the

current trend is oriented towards much smaller as well as geographically

widely dispersed power generation units [11]. In the new Smart City

paradigm energy should be “clean and sustainable”, as well as “available

all the time” keeping in mind the economic feasibility.

Distributed Generation (DG), is increasingly associated with a more

sustainable type of power supply. Adoption of composite multi- generation

systems may yield significant benefits in terms of energy efficiency and

reduction of carbon emissions, due to the fact that DG combines

geographically dispersed decentralised generation from preferably

renewable energy sources (RES). DG can also help with the reduction of

transmission losses and problems related to congestion in energy

distribution systems, while providing appropriate power quality (exergy)

for different types of end users [43,44]. Further, the use of combined heat

and power (CHP) technologies can enable a rational use of thermal energy

that would normally be wasted and thus it can determine a reduction in

primary energy use and carbon emission. This is fundamental especially

for city users who have a large heating demand with respect to electricity

demand, both in civil and industrial sector.

The main strengths of DG paradigm are be summarized by Massimiliano

Manfren et al. [43] as follow:

1. Power generation from large variety of distributed resources

together with the exploitation of local micro-sources with benefits

in term of decreasing fossil fuels dependence and protection against

electric system’s failure

2. Optimal generation, distribution and storage management to meet

specific needs in the built environment

3. Market accessibility for small investors

4. Direct customers’ involvement in energy demand and peak power

reduction programs.

Figure 2.11 shows the projected capacity of DG technologies in the

Annual Energy Outlook 2013 [45].

2 Definitions and characteristics of high performance buildings

38

Figure 2.11 Installed buildings sector DG capacity in Annual Energy Outlook 2013

Reference case (Gigawatts) [45]

2.2.1 Technologies used in Distributed Energy

Different solutions can be successfully implemented in the Smart City; and

in its building. Table 2.4 summarizes the differences and advantages of the

studied technologies

Table 2.4 Comparison of most common distributed energy sources [46]

Generator Power Dispatchable Efficiency* Common Application

Elec. Th.

Solar PV X - No L Household, Buildings

Solar TC - X No M Household, Buildings

Solar CSP X X Yes M District for Thermal

District, Power Plant

for Electricity

Solar PV/T X X No M-H Household, Buildings,

District

Windpower X - No M District, Power Plant

Poly-gen X X Yes H Building, District

Biomass X X Yes M Household, Building,

District for Thermal

District, Power Plant

for Electricity

Geothermal X X Yes H Household, Building,

District for Thermal

District, Power Plant

for Electricity

* L: Low (<30%); M: Moderate (<60%); H: High (>60%)

2 Definitions and characteristics of high performance buildings

39

Photovoltaic solar energy is the direct conversion of sunlight into

electricity. The basic building block of a PV system is the solar module,

which consists of a number of solar cells. Solar cells and modules come in

many different forms that vary greatly in performance and degree of

maturity. Applications range vary from small-scale systems for rural use

(tens or hundreds of watts), to building-integrated systems (kilowatts).

Solar PV panels can be integrated in the building surface or sometimes

even replacing other envelope materials and the electricity generated can

be used locally or send to the distribution network; however they have low

efficiency the best possible technology reaches the efficiency of 43.5%

(see Figure 2.12), and so they still are expensive at utility scale. With the

use of an energy storage the electricity produces from solar PV becomes

dispatchable.

Figure 2.12 Trends in conversion efficiencies for various solar cell technologies [21]

Thermal Collectors (TC) are perhaps the simplest way to use solar

resources. These systems can be active or passive. In active conversion

systems, heat from a solar collector is transported to the end process by a

heat transfer system. In passive systems, no active components are needed

to use the solar resource for heating. The most used systems are the active

systems that convert sunlight to thermal energy for water heating, space

heating, and eventually space cooling.

2 Definitions and characteristics of high performance buildings

40

Most active solar energy technologies have four basic components:

Solar thermal collector(s), flat plate and evacuated tube collectors

are the most typical

Storage system, in order to meet the thermal energy demand when

solar radiation is not available

Heat transfer system, piping and calves for liquids and ducts and

dampers for air; pumps, fans, and heat exchangers, if necessary

Control system, to manage the collection, storage, and distribution

of thermal energy.

Figure 2.13 shows the application of Thermal Collectors in different world

countries.

Figure 2.13 Applications of glazed and evacuated tube collectors, by region, [21]

Concentrated Solar Power (CSP) plants use mirrors that reflect and

concentrate sunlight onto receivers. The receivers convert the solar energy

to thermal energy, which is also used in a steam turbine to produce

electricity. CSP plants are not installed on building but in its surroundings

due to the plant greatness. Additionally, the photovoltaic-thermal collectors

(PV/T) have higher efficiency; but there are few commercial modules and

only in small scale.

Wind energy technologies can be classified into two categories: macro

wind turbines that are installed for large-scale energy generation such as

wind farms, and micro wind turbines used for local electricity production.

Micro wind turbines are suitable for application at the building scale and

are called “building-integrated wind turbines” [47]. Unlike macro wind

turbines, “building integrated wind turbines” have some additional

problems as explain by Jialin Wang [48], in particular the reduced size,

2 Definitions and characteristics of high performance buildings

41

which leads to lower performance (lower Re number). These turbines that

operate near buildings have more possibility to have failures and

malfunctions too. In addition as attached equipment to the building, wind

turbine brings many influences besides more energy. Some of these

influences might affect the comfort of the built environment like peace or

sunshine; while the others might affect the original structure of the

building.

Biomass is a topic of increasing importance in recent years. It is a very

versatile energy source capable of providing heat, electricity and fuels at

competitive prices, through different process (see Figure 2.14).

Within the conversion technologies, biomass combustion is the most

mature and market-proven technology. The main advantage of biomass

combustion is the relatively high efficiency of modern furnaces and the

economic feasibility of bioenergy projects. However, problems still occur

due to changing fuel proprieties and unstable operation of biomass

combustion systems [21]. Biomass could also be converted through

gasification, pyrolysis, anaerobic digestion and fermentation in synthesis

gas (syngas), and used in Organic Rankine Cycle (ORC) to produce

heating and electricity.

Figure 2.14 Main conversion routes for biomass to secondary energy carriers [21]

However, farming of biomass need to be done in responsibly manner in

order to be sustainable.

2 Definitions and characteristics of high performance buildings

42

Geothermal energy can be used for thermal only production (low-medium

temperatures) or cogeneration (high temperatures). The most recent use of

low-grade geothermal energy is in the form of ground source heat pumps

that use the natural temperature of Earth (between 5° and 30°C) to produce

both heating and cooling and domestic hot water for homes, schools,

government and commercial buildings with a limited amount of electric

energy input, required to run a compressor. However, the energy output is

about four times the energy input in electricity form (COP ~ 4) [21].

Geothermal Heat Pumps (GHPs) come in two basic configurations:

ground-coupled systems, which are installed in the ground, and

groundwater systems (open loop), which are installed in wells and lakes. In

the ground-coupled system, a closed loop of pipe, placed either

horizontally or vertically is placed in the ground, and water antifreeze

solution is circulated through the plastic pipes to either collect heat from

the ground in the winter or reject heat to the ground in the summer. The

open loop system uses groundwater or lake water directly in the heat

exchanger and then discharges it into another well, into a stream or lake, or

on the ground, depending on local laws. Geothermal power is generated by

using steam or a hydrocarbon vapour to turn a turbine-generator set to

produce electricity. A vapour-dominated resource ( see Figure 2.15a) can

be used directly, but a hot water resource ( see Figure 2.15b) needs to be

flashed by reducing the pressure to produce stream. Some plants use

double and triple flash to improve efficiency. Usually a wet or dry cooling

tower is used to condense the vapour after it leaves the turbine to maximize

the temperature and pressure drop between the incoming and outgoing

vapour and thus increase the efficiency of the operation.

Figure 2.15a Steam plant using a vapour or dry

steam dominated geothermal source [21] Figure 2.15b Single stage flash plant using a

water dominated geothermal resource

separator to produce steam [21]

2 Definitions and characteristics of high performance buildings

43

More recently the use of combined heat and power plant is developed, due

to maximize the use of the geothermal energy. The vapour leaving the

turbine exchanges heat with a district heating network, which brings heat

to different users (see Figure 2.16).

Figure 2.16 Cascading the use of geothermal resource for multiple application [21]

Finally poly-generation or Distributed Multi-Generation (DMG) can be

seen as a concept that generalizes DG to multiple energy vectors, capturing

at the same time the possibility of increasing generation efficiency from

DG thermal power plants and integrating a number of multi-energy

distributed resources that are locally available. In particular,

decarbonisation of domestic heat is major topic in many countries [44].

The simple example of DMG system is a combined Heat and Power (CHP)

plant. CHP, also known as cogeneration, means that both electrical and

thermal energy are generated simultaneously. The significant benefit is the

overall efficiency, which cab as much as 85-90% [47]. Conventionally,

CHP plants have been large-sized, centralized units. Steam and heat

produced by these plants can be utilized in industrial processes and district

heating. A new trend is towards distributed CHP, if the electrical power

produced by the plant is less than 200 or 100 kW, the terms small-scale

distributed CHP and micro-CHP are used respectively [49]. One of the

most promising targets in the application of CHP lies in energy production

for buildings. The relevant competing technologies in this regard are

reciprocating engines, micro-turbines, Stirling engines, and fuel cells, Kari

Alanne and Arto Saari [50], analyse the technical features of each of these

technologies (see Table 2.5). In the smallest size, fuel cells and Stirling

engines are regarded as the most applicable technologies. The benefit of

2 Definitions and characteristics of high performance buildings

44

these technologies is their ability to utilize sustainable fuels, like biomass

or natural gas. In addition, CHP plants may represent extremely flexible

resources, particularly if coupled with thermal storage.

Table 2.5 Technical features of small-scale CHP devices [50]

Reciprocating

engines

Micro-turbines Stirling engines Fuel cells

Electrical power

(kW)

10-200 25-250 2-50 2-200

Electrical

efficiency full

load (%)

25-45 25-30 15-35 40

Electrical

efficiency half-

load (%)

23-40 20.25 35 40

Total efficiency 75-85 75-85 75-85 75-85

Electrical

power/heat flow

0,5-1,1 0,5-0,6 0,3-0,7 0,9-1,1

Output

temperature level

(°C)

85-100 85-100 60-80 60-80

Fuel Natural or

biogas, diesel,

fuel oil

Natural or

biogas, gasoline,

alcohols

Natural or

biogas, several

liquid or solid

fuels

Hydrogen,

gases

including

hydrogen,

methanol

Length of

maintenance

cycle (h)

5000-20000 20000-30000 5000 -

While cogeneration refers to the production of two energy vectors,

combination of relevant pieces of equipment can allow extension to

multiple energy vector production and in particular trigeneration. A

classical trigeneration scheme is for production of electricity heat and

cooling in so-called Combined Cooling Heat and Power (CCHP) plants.

Classical CCHP layouts envisage coupling of an adsorption chiller in a

bottoming cycle to a CHP pant. In this way, heat that is recovered in

cogeneration is used in the adsorption chiller to produce cooling. In turn

this allows increase in the annual energy performance of the overall system

and improvement of the business case for distributed system.

It is also important remember that the integration of DG in the distribution

network is a difficult process ,and that widespread introduction of

renewable resources in DG will have a significant impact on both the

electrical system and the electricity market. In fact, DG installation affects

power quality in various ways. One of the major impacts of DG on power

45

quality is its effect on the functioning of over–current protection schemes,

among which the most important related events are voltage dips [51].

For this is necessary to keep on the researches on Smart Grid as shown in

[51-53]. In this thesis we don’t talk about Smart Grid, as the topic does not

fill in the energetic analysis proposes forward.

46

3 The CONCERTO project experience

47

3 The CONCERTO project experience

3.1 Introduction

CONCERTO is a European Union (EU) initiative developed within the

European Research Framework Programme (FP6 and FP7) and extended

by Horizon 2020. Responding to the facts that buildings account for 40%

of total energy consumption in the Union, for 33 % of CO2 emissions and

that 70% of the EU’s energy consumption and a similar share of

greenhouse gas emission take place in cities, with a huge untapped

potential for cost-effective energy savings, CONCERTO aims to

demonstrate that the energy optimisation of districts/neighbourhoods and

communities as a whole is more cost-effective than optimising each

building individually, if all relevant stakeholders work together and

integrate different energy-technologies in a smart way. Sustainable energy

neighbourhoods, such as those created under CONCERTO, are powerful

showcases for demonstrating that an energy transition is not a burden but

an opportunity. While making communities less dependent on energy

imports and more resilient against energy price increases.

The EU initiative started in 2005 under of the European Commission’s

General Directorate for Energy and has co-founded, more than 175,5

Million €, to 22 project in 58 cities and communities in 23 countries. The

CONCERTO cities, communities and the associated projects are extremely

diverse. This is both in terms of their climates their socio-economic make

up and most importantly their energy needs (see Figure 3.1) [54-55].

Concerto is a milestone towards the EU energy targets for 2020 (already

cited in Chapter 1).

Each CONCERTO project consists of one to four sites which are specific

defined geographical areas. The first generation of projects started in 2005,

the second in the end of 2007 and the third generation of projects started in

2010. The average duration of projects is five years in which communities

and sites elaborate and implement diverse activities toward the goals of the

CONCERTO initiative.

CONCERTO projects were precursors to the EU’s Smart Cities Initiative,

which continue to explore cost-efficient ways to transform European cities

into energy-efficient and sustainable neighbourhoods. The Smart Cities, as

we said previously, start from the CONCERTO approach and extends it to

include transport, grid and ICT.

The main common goal of the CONCERTO cities is to significantly reduce

their CO2 emissions in the most cost effective way, whilst at the same time

greatly improving the living habitats for their citizens.

3 The CONCERTO project experience

48

Figure 3.1 Map of CONCERTO cities [54]

From an energy perspective, this can only be achieved by a combination

of measures which simultaneously focus on reducing energy demand,

increasing the share of renewable energy sources on the energy supply

side, and using efficient energy conversion systems. The CONCERTO

cities demonstrate that through a holistic approach and through the

application of smart, well defined energy concepts appropriate for the

specific context of the district or city, emissions can be saved much more

effectively than by looking only at a series of single measures e.g. targeting

only energy efficiency in buildings or focusing only on the most efficient

renewable energy supply, as explained in Figure 3.2 [56].

Therefore CONCERTO initiative is unique, as it takes both individual

buildings as well as entire district into account, regarding energy efficiency

measures, energy production and energy distribution. CONCERTO cities

3 The CONCERTO project experience

49

and communities demonstrate new, realistic models that are close to being

zero energy communities.

Figure 3.2 Idea of CONCERTO project [56]

Grenoble’s district of Caserne de Bonne is a perfect example of

CONCERTO ideas. In the SESAC project (www.concerto-sesac.eu), nine

new multi-storey buildings with 435 apartments were built in de Bonne.

These buildings are supplied with heat and electricity by nine mini CHP

plants with the expected final energy consumption being 30-40% below the

applicable national indices. The design of these buildings includes compact

dimensions, specific insulation values, double-flow ventilation, as well as

water and light efficient equipment. A special landmark in de Bonne is the

positive energy office building, which produces more energy than it

actually needs. The building was designed with a high compactness, an

efficient external insulation a high share of natural lighting and special

attention was taken to avoid thermal bridges. The windows have very low-

emissive triple glazing and automatic, movable external solar protections

with tilting louvers. Furthermore a special innovative, internal shutter

system has been integrated that is mechanically movable between the

ceiling and the windows. Table 3.1 gathers the main results of the SESAC

project.

Table 3.1 SESAC project facts and results [57]

Facts and Results

Estimated population involved 35000

Geographical area 28 ha

Total investments 65,5 € million

CONCERTO funding 2,1 € million

New buildings 15 (60300 𝑚2)

Energy supply unit (ESU) Solar thermal collectors 752 kW

Biomass boilers 2380 kW

Photovoltaics 363 k𝑊𝑝𝑒𝑎𝑘

3 The CONCERTO project experience

50

Micro CHP 1040 k𝑊𝑡ℎ𝑒𝑟𝑚 580k𝑊𝑒𝑙

District heating network

Energy demand (average) New buildings 64 kW/𝑚2/yr for heating

and domestic hot water (DHW)

(-45% compared to national standard)

𝐶𝑂2 emission reduction [ton/year] 1508

3.1.1 CONCERTO buildings (Demand Side)

Demand side measures applied in the CONCERTO cities, as said in [56]

focus on the well-known principles of optimising:

the building structure and properties in order to reduce energy

needs as much as possible

the design of heating, cooling and ventilation systems, controls and

building management systems in order to reduce expected energy

use as much as possible

building operation and in particular user behaviour mainly by

motivating end-users to use their building in an intelligent way,

while increasing the comfort.

Figure 3.3, for examples, illustrates the reduction in delivered energy

through the actions implemented in the retrofitting of a Historical

Childcare Building in North Tipperary (IR). It shows the effect of each

energy demand measure on the energy performance of the building.

Buildings in Concerto Demonstration projects undergo ambitious

requirements. The energy consumption of new buildings need to be at least

30% lower than national regulations. For existing building, after

refurbishment or retrofitting, the CONCERTO approach requires lower

energy consumption per m2 than the national regulation for new buildings.

About 1400 buildings have been refurbished in CONCERTO projects and

more than 3000 new houses have been built [57].

CONCERTO provides also the following example of NZEBs: Positive

Office Building in Grenoble (FR) and Energy-Plus House in Stenlose (DK)

[58].

3 The CONCERTO project experience

51

Figure 3.3 Energy performance achieved after implementation of measures (In a

Childcare Facility in North Tipperary)

The demonstration objects vary from newly erected smaller and larger

house to the retrofitting of big blocks of flats and single family homes.

Within some areas, the social mixture of the districts is taken into special

account meaning that whilst some of the houses consist of high-end flats,

some others comprise public housing. In other areas, large blocks of flats

are retrofitted, integrating the residents into the process and ensuring that

the measures taken will also have a positive payback for them and the

consequence will not be the social upgrading of the area. What unites the

projects are ambitious goals in terms of energy and living comfort and a

broad involvement of the different stakeholders: residents, energy

suppliers, local governments and residential building cooperatives [57].

Some examples of retrofitting projects within CONCERTO can be found

in Table 3.2.

Table 3.2 Selected examples of retrofitting projects within CONCERTO

Site Details

Ajaccio (FR) Buildings from 1960s were made more energy efficient; double-

glazing with thermo-coating has been installed in fifty buildings in

the historic city centre. Altogether 250 buildings have been re-

glazed in this way.

Amsterdam

New West (NL)

A total of 500 dwellings in 4 buildings (built in the 1960s) were

refurbished by the project, providing a blue-print for 50000 similar

dwellings that could be renewed in the same way in future.

Country of North

Tipperary (IE)

Basic measures to around 400 rural dwellings across the county as

well as the retrofitting of an agricultural college were realised.

41

41

49

160

249

340

0 50 100 150 200 250 300 350 400

Final Building

Low Energy Lightening

Upgrade of Heating System and Controls

Roof Insulation

Wall Insulation

Existing Building

Kwh/m^2/year

Energy Performance

3 The CONCERTO project experience

52

Galanta (SK) Three 8-storey residential buildings with 32 dwellings and a school

was retrofitted.

Hannover (DE) Refurbishment of 35 multi-occupancy buildings with 350 dwellings

plus some detached houses.

Montieri (IT) The project aims to connect private and public buildings to a

geothermal district heating network to serve 425 users within the

historic village of Montieri. Part of these dwellings will receive

energy efficiency improvements and solar hot water systems as well.

Lembeth, London

(UK)

Refurbishment of inner city tower blocks serving as social housing.

El picarral,

Zaragozza (ES)

Seventy dwellings in El Picarral, a working class neighbourhood,

received a comprehensive modernisation work, including new

heating systems, insulation and lifts.

Neckaesulm (DE) High efficiency retrofit of the primary school Neubergschule (gross

floor area of 2.862 𝑚2). Besides retrofitting the building envelope a

new heating system was installed consisting of two pellet boilers

with 69 kW each and a prototype Stirling engine. The primary

energy demand has been reduced by 75%. When taking the

electricity produced by the PV system on the school roof and by

Stirling engine into considerations, the primary energy balance

comes close to being zero.

Budapest (HU) Retrofitting of the Obuda’s “village”. During the retrofitting work,

the apartments were given a complete insulation makeover. The

1800 old windows were replaced by new, five chamber plastic

windows and the heating system was renewed. Heat is now

delivered by the local district heating network.

Beside residential buildings CONCERTO projects include public

buildings. Public demonstration buildings have the advantage of teaching

and showing people first-hand about possible improvements. If the

community acts as a role model for new approaches, its citizens are often

more easily convinced to take actions themselves. The dissemination of

successful measures is sometimes supplemented by permanent exhibitions

or electronic displays, showing the produced or saved energy. In particular

schools represent an ideal location to link the topic “energy transition” with

education and training. In addition to them being a suitable location for

undertaking energy efficiency measures and installing renewable energy

resources, they also provide a valuable means for engaging children,

parents, teachers in many issues relating to climate change.

For example, a holistic “solar for school” package has been developed and

delivered to the six school of Lambeth city (UK). The package included the

installation of solar photovoltaic and thermal panels that feed directly into

the school’s electricity and heating systems. In addition to the PV and

thermal panel installations, an extensive programme has been carried out in

the schools, teaching the students directly how to save energy.

Other examples in addition to the already mentioned, Lambeth City

schools and Childcare Facility in North Tipperary could be found in [57].

3 The CONCERTO project experience

53

3.1.2 CONCERTO energy supply units (Supply Side)

The CONCERTO cities applied a whole range of supply side measures

based on the use of following renewable energy sources, including the

following key technologies [57]:

Photovoltaic and solar heating systems (both individual and connected to

district heating networks), often architecturally integrated. The sun has

played a large role inside the renewable energy sources used in

CONCERTO projects. The use of solar thermal energy for hot water

generation is a very suitable and efficient way to decrease the consumption

of fossil energy sources. Whereas many of the 58 communities within

CONCERTO have installed small demonstration plants on single

buildings, ten sites have implemented large plants. Altogether, 744 solar

thermal systems have been installed so far, with a total area of 32.400

m2 and a total thermal power of 22,7 MW. Moreover a total of 365 small,

building-related photovoltaic systems with a total peak power of 4,4 MW

have been installed so far. Forty-one plants with a total peak power of 5,5

MW complement the photovoltaic implementations in CONCERTO.

Heath Pumps, there are several way to approach the integration of heat

pumps into urban energy systems. They are a very well-known solution for

heating and cooling purposes. They are composed by two exchangers: in

winter, the heat exchanger located outdoors will absorb heat from the

environmental air, transferring it to the indoor exchanger to heat the indoor

environment; and, in summer, the role of each part is inverted. These

devices can be used to produce heated and cooled fluids with particularly

high efficiency rates (COP ≈ 3). Heath pumps performance depends on

both indoor and outdoor temperatures; the smaller the difference between

those two values, the higher the efficiency of the heat pumps. Therefore, it

is convenient to reduce the difference between them as much as possible. A

possible solutions is to use ground as a source in winter and a sink in

summer, since at a certain depth the ground temperature doesn’t suffer

significant fluctuations throughout the year (Ground coupled heat

pumps), in this case the electricity consumption could be 25% lower than

the case of an conventional air pump. Heath pumps have more advantages

when the electricity enter the pumps is generated from renewable sources.

In CONCERTO were installed a total thermal and cooling power of 5,8

MW generated from heat pumps powered by renewable energy.

Biomass boilers and combined heat and power units (mainly large scale

systems connected to district heating) Several polygeneration

technologies are taken into consideration due to their ability to save

primary energy sources. The term polygeneration describes a process with

the output of more than two products and can include a material in addition

to energy e.g. electricity, heat and cleaned biogas from a biogas CHP-plant

3 The CONCERTO project experience

54

or electricity, heat and cold from a CHP plant, in combination with

adsorption chillers. Larger combined heat and power plants were all

connected to district heating networks guarantee the proper use of heat

generated. In CONCERTO, various CHP and polygeneration plants have

been installed, ranging from microCHP units (driven by conventional

fuels) for use in residential buildings over Organic Rankine Cycle (ORC)

plants in combination with adsorption cooling, up to large biogas plants,

producing electricity, heat and fertiliser. The smallest CHP in

CONCERTO has a power of 12.5 𝑘𝑊𝑡ℎ and of 5.5 𝑘𝑊𝑒𝑙. The largest CHP

plant, in combination with biogas production, provides 28MW of electrical

power and 68 MW of heat. Some innovative biomass CHP systems, like

sterling and linear generators, have been tested and demonstrated in

CONCERTO as well.

Adsorption cooling, the term “sorption cooling” (using the physical

process of absorption or adsorption) refers to generating cooling energy

from heat. This method offers the possibility to provide cooling by use

solar energy or heat from renewable energy sources, at a time when

cooling is needed the most. As the energy source solar heat or waste heat

can be used, the emission of CO2 can be reduced significantly compared to

electric compression chillers.

Wind turbines, in the field of electricity production, large wind power

plants have also shown good results and have contributed a large part of

the locally produced energy. Whilst the larger plants are usually situated

outside the communities, small wind turbines bring wind energy to an

urban scale. Several urban wind turbine projects have been planned, but so

far only three have been finished. 14 wind turbines have been installed for

a total installed power of almost 40 MW.

Waste water use and Biogas generation plants, There are a lot of

different waste types that can be used for energy production. Waste water

can be used as a source of energy in two different ways: either as a thermal

source for driving heat pumps or for fermenting material for deriving

biogas. As example, we could observe the project in Weiz-Gleisdorf (AU)

community, where a wastewater heat pump system has been developed.

This system is the first heath pump in Austria to use heat from a sewage

treatment plant. The power of the heat pump is 410 kW.

Communal tree-surgery and garden waste, as well as agricultural waste,

can be used for producing biogas as well. When dehumidified, these

materials can also be used for heat generation by biomass boilers.

Domestic waste, however, is thermally used in special waste incineration

plants.

District heating/cooling systems Within CONCERTO, four cooling

networks and 94 heating networks have been included in the demonstration

activities. Whereas 88 of them have been newly developed, ten of them

3 The CONCERTO project experience

55

have been extended by additional energy supply units or by connecting

additional buildings. The capacity ranges from small networks, with a few

kilowatts connecting three or four houses, up to several megawatts

connecting hundreds of buildings.

Geothermal Plants, some CONCERTO communities make use of

geothermal energy, but in varying ways. There are two different

approaches: close to surface and deep geothermal utilisation. The close to

surface system usually require a heat pump to raise the temperature to a

higher level to meet the level required for heating. Deep geothermal use is

instead not economic for a single buildings, but is used for geothermal

plants that use steam to drive turbine for electricity generation on a large

scale. It is also possible reutilise former mining shafts and lake water or

sediment from a river for low temperature cooling and heating.

Store energy, the challenge of using solar and wind energy is that supply

often differs from demand. On sunny days, there is not much requirement

for heating energy. This leads to a surplus supply, whereas during the

winter time when more heating energy is required, a smaller, insufficient

amount of solar energy is available. The challenge is to develop efficient

storage system. CONCERTO research activities concentrate on large-scale

thermal storages able to store energy for long period of time. For future

work it is advisable start research on small efficient storage suitable for

buildings.

Such an integrated approach has been the ambitious task of the

CONCERTO communities. By applying the optimal mix of technologies

and renewable energy sources to demonstration activities, it could be

proven that innovative solutions and new products are mature enough to

take on the fight against climate change and to pave the EU’s way towards

CO2 neutrality.

CONCERTO projects not only built RES based system, but adapted the

degree of centralisation of an energy supply infrastructure to the energy

demand intensity. In CONCERTO, distributed and scattered energy supply

systems were mostly used in regions with low settlement density. On the

other hand, centralised energy supply systems were traditionally used in

city centres and highly populated areas with a high energy demand density.

It was found to be also necessary to select the right energy carrier quality

(low temperature heat, electricity, etc…) compatible with the requested

quality for the energy needs. This helped the CONCERTO cities to

combine the technologies while giving preference to optimal use of energy

carriers:

Low temperature systems for space heating applications

Medium temperature systems for hot water production

3 The CONCERTO project experience

56

Electricity for application which cannot be driven by another carrier

Moreover, heath pumps could be used to adjust the temperature to the

needs.

In CONCERTO projects, the smartest energy solutions for

neighbourhoods and districts were found considering these principles [56].

All 58 Communities have worked out concepts and implemented different

technologies to increase their share of renewable energy sources and to

decrease the emission of greenhouse gases in their demonstration areas. A

total of almost 390 MW of renewable energy power has been installed

within the CONCERTO programme so far (see Figure 3.4), which

corresponds to a total investment of 416,3€ million in the field of energy

supply and energy distribution; Figure 3.4 to Figure 3.7 presents a final

report about the RES installed in CONCERTO projects, divided for

different final use.

Recapping CONCERTO projects have led to [54]

More than 3.000 high-performance new buildings were built (1,75

million m²)

Around 1.400 buildings were refurbished (2 million m²)

376 kton of CO2 emission reductions per year in these areas

divided in:

17 kton in refurbished buildings

17 kton in new buildings

147 kton in energy supply units – electricity

173 kton in energy supply units – district heating

22 kton in energy supply units – heating

1.326 GWh non-renewable primary energy demand reduction per

year, in these areas:

5 % in refurbished buildings

6 % in new buildings

37 % in energy supply units – electricity

46 % in energy supply units – district heating

6 % in energy supply units – heating (if no data for buildings or

district heating is available)

530.000 tons of CO2 emissions saved per year

3 The CONCERTO project experience

57

Figure 3.4 Total installed RES power in CONCERTO [54]

Figure 3.5 Total installed RES electricity power in CONCERTO [54]

3 The CONCERTO project experience

58

Figure 3.6 Total installed RES heating power in CONCERTO [54]

Figure 3.7 Total installed RES cooling power in CONCERTO [54]

3 The CONCERTO project experience

59

3.2 Introduction to CONCERTO Premium Technical

Monitoring Database

The CONCERTO Premium Technical Monitoring Database

(http://concerto.eu/concerto/db-access.html#) manages data from 58

European communities, participating in the CONCERTO initiative (Table

3.3). One of the IT challenges is the divergence of the data collected from

the CONCERTO project sites. Some data fields are commonly used

throughout the CONCERTO initiative, but there are also data fields, which

are unique for a project site, depending on the country or community, in

which it is situated, and/or on the monitoring date. The CONCERTO

Premium Technical Monitoring Database addresses this challenge by using

a graphical output.

The main reason for introducing this tool were the following:

All users having access to the database are provided with the same

status of information.

Because of the high capacity of the database, original and non-

treated data can be stored on it. In particular, absolute figures of

energy use are given in order to avoid misunderstandings due to the

floor area definition in specific energy performance ratings. In a

similar way, degree day corrections or primary energy use

calculations are made in a second step on the basis of the non-

treated data.

The three procedures of data collecting, analysing and reporting are

integrated in one tool, which highly simplifies the assessment work.

As soon as the database is set up for each community, the

monitoring data transfer can be done easily.

The operative system of the Database is Neo4j, a high-performance,

enterprise grade graph database, Thomas Lutzkendorf et. al [55] explain

the specification of this operative system. It enables the Technical

Monitoring Database to use a flexible data model which considerably

facilitates the information management in CONCERTO Premium.

The indicators that have been proposed by CONCERTO Premium are

based on the three pillars of sustainability, i.e. the economic, the

environmental and the social dimensions. The process of indicator

identification and declaration aims at covering each dimensions [59]. The

pillars of sustainability should not be confused with the six pillars of the

Smart City described in chapter 1, because are different tool.

The majority of indicators are based on the energy flows entering or

leaving the object of interest, the definitions of energy flows depends from

elements like individual buildings, sets of buildings and energy supply

3 The CONCERTO project experience

60

units. Always Thomas Lutzkendorf et. al [55] give the definition of energy

flows and the formulary of the indicators. Table 3.3 58 cities of CONCERTO Database

Cities included in CONCERTO Premium Technical Monitoring Database

Ajaccio, Alessandria, Almere, Amsterdam New West, Amsterdam North, Apeldoorn,

Birstonas, Cerdanyola del Vallès, Cernier, Delft, Dundalk, Falkenberg, Galanta, Geneva,

Grenoble, Hannover, Hartberg, Heerlen, Helsinborg, Helsingør, Hillerod, Hvar, Høje-

Taastrup, Kortrijk, Lapua, London Lambeth, Lyon, Maabjerg, Milton Keynes, Montieri,

Mòrahalom, Mödling, Nantes, Neckarsulm, Neuchàtel, North Tipperary, Obuda,

Ostfildern, Redange, Salzburg, Sandnes, Slubice, Sofia, Stenloese, Szentendre, Torino,

Trondheim, Tudela, Tulln, Valby, Viladacans, Victoria-Gesteiz, Växjö, Weilerbach,

Weiz-Glesdorf, Zagorje, Zaragoza, Zlin

The Database consists of two main parts: the “consumption” part gathers

all data related to energy use (mainly buildings) and the “generation” part

focuses on the description of the energy supply infrastructure (district

energy systems and distributed energy plants). Both expected and metered

energy performance data is stored into the database, allowing for a

comparison between expected and actual community energy performance.

As visible in Figure 3.8 the indicators in the CONCERTO Premium

Technical Monitoring Database are divided in four group: buildings

indicators, energy supply units (ESU) indicators, city indicators and

country indicators, the latter is not taken into account in these thesis

because is not significant and there are few results in the database. For

each of these groups there are four topic: economic, environmental,

economic-environmental technical. In the following pages there are some

tables with the energy or energy-related indicators present in the

CONCERTO database.

Building

Indicators

CONCERTO Premium Technical Monitoring Database Indicators

ESU

indicators

City

Indicators

Country

Indicators

Economic

Environmental

Economic-Environmental

Technical

Economic

Environmental

Economic-Environmental

Technical

Figure 3.8 Structure of CONCERTO premium Technical Monitoring Database Indicators

3 The CONCERTO project experience

61

3.2.1 Buildings indicators

In CONCERTO a large number of demonstration activities has been

performed on buildings, including low-energy residential and non-

residential new construction as well as refurbishment of existing buildings.

2353 building objects are registered in the Technical Monitoring Database

and not over than 4500 as said in [55]. In order to get significant results the

buildings are classified by building type (residential, municipal, tertiary

non-municipal, industrial) and by building size (<180m2, 180-1000 m2,

1000-5000 m2 and > 5000m2). Table 3.4 gathers the buildings technical

indicators their measure unit and their availability in the database.

Table 3.4 Technical indicators for buildings

Indicator Unit Availability*

Final energy demand for space heating, based on

design/monitoring data.

kWh

m2yr

34,5%

Final energy demand for domestic hot water,

based on design/monitoring data.

kWh

m2yr

15,5%

Final energy demand for electricity (lighting,

ventilation, etc.), based on monitoring data.

kWh

m2yr

58,9%

Final energy demand for space cooling, based on

design/monitoring data.

kWh

m2yr

<0,1%

ΔFinal energy demand for space heating, based

on design/monitoring data.

kWh

m2yr

57,8%

ΔFinal energy demand for domestic hot water,

based on design/monitoring data.

kWh

m2yr

45%

ΔFinal energy demand for electricity (lighting,

ventilation, etc.), based on design data.

kWh

m2yr

31,7%

ΔFinal energy demand for space cooling, based

on monitoring/based data.

kWh

m2yr

<0,1%

Fossil primary energy demand for space heating,

based on design/monitoring data.

kWh

m2yr

60,1%

Fossil primary energy demand for domestic hot

water, based on design/monitoring data.

kWh

m2yr

53,8%

Fossil primary energy demand for non-heating-

cooling electricity, based on design data.

kWh

m2yr

58,4%

Fossil primary energy demand for space cooling,

based on design/monitoring data.

kWh

m2yr

<0,1%

ΔFossil primary energy demand for space

heating, based on design/monitoring data.

kWh

m2yr

57,8%

ΔFossil primary energy demand for domestic hot

water, based on design/monitoring data.

kWh

m2yr

48,6%

ΔFossil primary energy demand for non-heating-

cooling electricity, based on design data.

kWh

m2yr

49,2%

3 The CONCERTO project experience

62

ΔFossil primary energy demand for space

cooling, based on design/monitoring data.

kWh

m2yr

<0,1%

Renewable primary energy demand for space

heating, based on design/monitoring data.

kWh

m2yr

61%

Renewable primary energy demand for domestic

hot water, based on design/monitoring data.

kWh

m2yr

53,5%

Renewable primary energy demand for electricity

(lighting, ventilation, etc.), based on design data.

kWh

m2yr

58,9%

Renewable primary energy demand for space

cooling, based on design/monitoring data.

kWh

m2yr

<0,1%

ΔRenewable primary energy demand for space

heating, based on design/monitoring data.

kWh

m2yr

44%

ΔRenewable primary energy demand for domestic

hot water, based on design/monitoring data.

kWh

m2yr

38,5%

ΔRenewable primary energy demand for

electricity (lighting, ventilation, etc.), based on

design data.

kWh

m2yr

28,5%

ΔRenewable primary energy demand for space

cooling, based on design/monitoring data.

kWh

m2yr

<0,1%

Floor area of high performing eco-buildings

constructed: new building. m2 43.4%

Floor area of high performing eco-buildings

constructed: refurbished building. m2 39,8%

*The indicator’s availability specified in how many cases of all those in the

Database, the indicator is presented and calculated. The availability is

considered only for design data as the monitoring data are still being

compiled.

As we can see from the table above, the database is far from being

completed, in fact only 7 indicators out of 29 are available at least for half

of the buildings. Moreover the indicators for space cooling are very poor;

in most of the cases the availability is lower than 0.1 % (only 15 buildings

available).

In the database there is also a repeat of the indicators of Final energy

demand reduction. In fact, in addition to those presented in Table 3.4, is

present another indicator called Final energy demand reduction per m2

based on monitoring data. Since the indicators presented above are already

measured in kWh

m2yr, these indicators are therefore the same thing.

This is a first demonstration of how the database is filled in an

approximate manner. Later in this chapter the main problems of the

CONCERTO Technical Monitoring Database will be brought to light.

Table 3.5 instead, gathers the buildings’ environmental indicators.

3 The CONCERTO project experience

63

Table 3.5 Environmental indicators for buildings

Indicator Unit Availability* CO2

CO2 equivalentNOxPM10PM2.5 SO2

SO2 equivalent}

kg

m2yr

73,1%

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

kg

m2yr

70,1%

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

kg

m2yr

69,9%

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

kg

m2yr

<0,1%

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

kg

m2yr

60,9%

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

kg

m2yr

53,8%

ΔEmissions for space

heating, based on

design/monitoring

reduction for space

ΔEmissions for domestic

hot water based on

design/monitoring

ΔEmissions for electricity

(lightening, ventilation, etc.),

based on design data, based

on design/monitoring

ΔEmissions for space

cooling, based on

design/monitoring

reduction for space

Emissions for space

heating, based on

design/monitoring

Emissions for domestic hot

water based on

design/monitoring

3 The CONCERTO project experience

64

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

kg

m2yr

58,39

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

-

kg

m2yr

<0,1%

*The indicator’s availability is calculated only for 𝐶𝑂2, considered the

most important emissions of this group; the other emissions indicators are

always less available.

Also in this case the indicators of space cooling are few and not indicative.

This indicates that CONCERTO focuses mostly on space heating, domestic

hot water and electricity, while cooling is neglected.

Table 3.6 shows the economic indicators for building present in the

CONCERTO database.

Table 3.6 Economic indicators for buildings

Indicator Unit Availability

Annuity of energy costs for space heating based

on design/monitoring data.

€

m2yr

61%

Annuity of energy-related additional capital costs €

m2yr

<0,1%

Annuity of grant €

m2yr

56,2%

Grants €

m2

56,2%

Equivalent price of final energy based on

design/monitoring data

€

kWh

23,2%

Equivalent price of fossil primary energy, based

on design/monitoring data

€

kWh

22,9%

Equivalent price of total primary energy, based on

design/monitoring data

€

kWh

22,4%

Costs of adopted energy saving measures €

m2

<0,1%

Dynamic payback period based on

design/monitoring data

year 21,5%

Also buildings’ economic indicators are present only for less than half of

buildings of the CONCERTO project. In particular capital costs of adopted

Emissions for electricity

(lightening, ventilation,

etc.), based on design data,

based on design/monitoring

Emissions for space

cooling, based on

design/monitoring

reduction for space

3 The CONCERTO project experience

65

energy saving measures is an important indicators which is completely

ignored by the database.

At least Table 3.7 shows the economic-environmental indicators for

buildings.

Table 3.7 Economic-environmental indicators for buildings

Indicator Unit Availability* CO2

CO2 equivalentNOxPM10PM2.5 SO2

SO2 equivalent}

€

𝑡𝑜𝑛

19,3%

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

kg

€

47,8%

Final energy demand reduction per grant for

space heating based on design/monitoring data

kWh

€

47,8%

Fossil primary energy demand reduction per

grant for space heating based on

design/monitoring data

kWh

€

47,9%

Renewable primary energy demand reduction per

grant for space heating based on

design/monitoring data

kWh

€

38,9%

3.2.2 Energy supply units indicators

Within the CONCERTO demonstration projects we find different types of

energy supply units. They have been grouped into building integrated

energy supply units (BIES) such as small solar thermal collectors or small

biomass boilers, and into community energy supply units (CES) such as

large combined heat and power (CHP) plants, wind turbines biogas plants

and large-scale storages. Also district heating networks, which join the

buildings to the supply units, belong to the second group [55].

In CONCERTO Technical Monitoring Database there are 2854 different

ESU. The indicators for ESU are reported from Table 3.8 to Table 3.11,

following the order of the buildings indicator.

Emissions abatement costs

based on

design/monitoring data

ΔEmissions per grant

based on

design/monitoring data

3 The CONCERTO project experience

66

Table 3.8 Technical indicators for ESU

Indicator Unit Availability*

Annual average power kWaverage

kWmax

94,3%

Efficiency kWh

kWh

94,3%

Annual energy output, based on

design/monitoring data

kWh

yr

66,6%

Annual final energy demand, based on

design/monitoring data

kWh

yr

94,3%

Final energy demand per kWh output, based on

design/monitoring data

kWh

kWh

94,3%

Annual Δfinal energy demand , based on design

data

kWh

yr

18%

ΔFinal energy demand per kWh output, based on

design data

kWh

kWh

18%

Annual fossil primary energy demand, based on

design/monitoring data

kWh

yr

94,3%

Fossil primary energy demand per kWh output,

based on design/monitoring data

kWh

kWh

94,3%

Annual ΔFossil primary energy demand, based

on design/monitoring data

kWh

yr

60,1%

ΔFossil primary energy demand per kWh output,

based on design/monitoring data

kWh

kWh

73%

Annual total primary energy demand, based on

design/monitoring data

kWh

yr

94,3%

Total primary energy demand per kWh output,

based on design/monitoring data

kWh

kWh

94,3%

Annual Δtotal primary energy demand , based on

design data

kWh

yr

42,8%

ΔTotal primary energy demand per kWh output,

based on design/monitoring data.

kWh

kWh

70,8%

*The indicator’s availability indicated in how many cases the indicator is

presented and calculated, considering all the Database projects. The

availability is considered only for design data as the monitoring data are

still being compiled.

The ESU’s technical indicators have a very high availability, about the

74%. The only indicators with little data are the final energy demand

reduction.

3 The CONCERTO project experience

67

Table 3.9 Environmental indicators for ESU

Indicator Unit Availability* CO2

CO2 equivalentNOxPM10PM2.5 SO2

SO2 equivalent}

kg

yr

60,1%

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

kg

kWh

72,6%

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

kg

yr

94,6%

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

kg

kWh

94,6%

*The indicator’s availability is calculated only for 𝐶𝑂2, considered the

most important emissions of this group; the other emissions indicators are

always less available.

Table 3.10 Economic indicators for ESU

Indicator Unit Availability

Annual Δenergy cost based on design/monitoring

data

€

yr

59,1%

ΔEnergy costs per kWh output based on design/

monitoring data

€

kWh

70,8%

Annual energy costs based on design/monitoring

data

€

yr

92,7%

Energy costs per kWh output based on

design/monitoring data

€

kWh

94,5%

Energy production costs €

kWh

94,5%

Grants €

kW

74,7%

Dynamic payback period Year 53,7%

ΔAnnual emissions, based

on design data

ΔAnnual emissions, per

kWh output, based on

design data

Annual emissions, based on

design data

Annual emissions, per kWh

output, based on design data

3 The CONCERTO project experience

68

Table 3.11 Economic-Environmental indicators for ESU

Indicator Unit Availability CO2

CO2 equivalentNOxPM10PM2.5 SO2

SO2 equivalent}

€

kg

53,7%

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

€

kWh

58,3%

CO2CO2 equivalent

NOxPM10PM2.5 SO2

SO2 equivalent}

kg

€𝑔𝑟𝑎𝑛𝑡

53,1%

ΔFinal energy demand per grant kWh

€𝑔𝑟𝑎𝑛𝑡

<0,1%

ΔFossil primary energy demand per grant kWh

€𝑔𝑟𝑎𝑛𝑡

53,3%

ΔTotal primary energy demand per grant kWh

€𝑔𝑟𝑎𝑛𝑡

26,9%

3.2.2.1 Focus on Technical Indicators of Buildings and ESU

In Figure 3.9 three examples of energetic system are presented to better

understand the main important Technical Indicators for Buildings and

ESU, which are essential for any energy analysis.

The energy system of the examples is formed by one building and one

ESU (e.g. boiler, PV, adsorption chiller etc.); there is also no distinction

between energy used for cooling, for space heating, for domestic hot water,

and for electricity, because the case is representative of all of these

situation.

In the first example we considered only the building, we can see that the

energy demand building is the energy entering the building in order to be

used in different areas of application (space heating, space cooling,

domestic hot water, electrical appliances). It can also be seen as the energy

that the building requires from the ESU.

Emissions abatement costs

based on design data

Emissions external costs

based on design

data/monitoring

ΔEmissions per grant based

on design data

3 The CONCERTO project experience

69

In the second case we considered also the ESU unit, we can see that the

Energy Output from the ESU, is the energy outing the ESU in order to be

used in the building in different areas of application, so the Energy Output

Figure 3.9 Examples of the flow of the Technical Indicators for Building and ESU:

the nomenclature refers to the one adopted in the CONCERTO technical monitoring

database.

from the ESU is equal to the Final Energy Demand of the building,

ignoring the transmission losses (in case of PV, solar thermal or other

building integrated units the transmission losses are nearly zero. They

become, instead more significant in case of big CHP, or heat/cold district

network). Instead The Final Energy Demand by the ESU corresponds to

the energy entering into the energy supply unit, in the form of Natural Gas

or electricity, which will then be converted in the energy output to the

building.

The last example adds the Primary Energy Demand, it corresponds to the

energy entering the ESU considered upstream of the supply chain.

Therefore the Primary Energy Demand of the building coincides with the

Primary Energy Demand of all of the ESU.

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3.2.3 City indicators

In the CONCERTO database there are some indicators for the 58 cities.

These indicators are the less significant of, because most of the cities has

only up to three active projects and these are not enough to influence the

entire city. City indicators are not divided in the four categories but

grouped together. The city indicators are reported in Table 3.12.

Table 3.12 City indicators

Indicator Unit Availability

Average capital costs per kW installed (Invoiced)

for electricity

€

𝑘𝑊ℎ𝑒𝑙

-

Average capital costs per kW installed (Invoiced)

for heating

€

𝑘𝑊ℎℎ𝑒𝑎𝑡𝑖𝑛𝑔

-

Average capital costs per kW installed (Planned)

for electricity

€

𝑘𝑊ℎ𝑒𝑙

-

Average capital costs per kW installed (Planned)

for heating

€

𝑘𝑊ℎℎ𝑒𝑎𝑡𝑖𝑛𝑔

-

Average capital costs per m2 for new buildings* €

𝑚2

-

Average capital costs per m2 for refurbishment

buildings*

€

𝑚2

-

Average final cooling energy demand reduction

per m2for new buildings*

kWh

𝑚2

-

Average final domestic hot water energy demand

reduction per m2for new buildings*

kWh

𝑚2

-

Average final domestic hot water energy demand

reduction per m2for refurbishment buildings*

kWh

𝑚2

-

Constructed floor area of high performing eco

buildings (New buildings)* 𝑚2 -

Constructed floor area of high performing eco

buildings (Refurbishment buildings) 𝑚2 -

Total capacity of installed cogeneration plants

MW electricity 𝑀𝑊𝑒𝑙 -

Total capacity of installed cogeneration plants

MW heating 𝑀𝑊ℎ𝑒𝑎𝑡𝑖𝑛𝑔 -

Total capacity of installed plants using RES MW

electricity 𝑀𝑊𝑒𝑙 -

Total capacity of installed plants using RES MW

thermal-heating and cooling 𝑀𝑊𝑡ℎ -

Total capacity of new commissioned supply units

using RES MW electricity 𝑀𝑊𝑒𝑙 -

Total capacity of new commissioned supply units

using RES MW thermal-heating 𝑀𝑊ℎ𝑒𝑎𝑡𝑖𝑛𝑔 -

Total capacity of new commissioned supply units

using RES MW thermal-cooling 𝑀𝑊𝑐𝑜𝑜𝑙𝑖𝑛𝑔 -

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3.3 Case studies: Energy and Urban Regeneration of the

Arquata District in the city of Torino

3.3.1 Presentation of the case studies

In the following analysis we will use the CONCERTO Premium Technical

Monitoring Database to analyse and compare the data of the project

POLYCITY of the Arquata district. The POLYCITY project has as main

objectives to reduce the consumption of fossil fuel trough energy efficient

buildings and to increase the use of renewable energies. The project

respectively supports different aspects of urban development in three

European cities: new buildings locations which are still underdeveloped in

the peripheral area of Barcelona, a mixture of re-development and new

building in Scharnhauser Park and the renewal of an old city district in

Turin. Major details on POLYCITY are available in the literature [58] .

The interventions in the Arquata district regard two typologies of buildings

(the ATC building and the social housing buildings) and local energy

supply system (district heating, CHP, solar power generation).

ATC Building

Figure 3.10 Planimetry of the Arquata District

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Figure 3.10 report a plan view of the Arquata district. The arrow indicates

the position of ACT buildings, the other buildings are the social housing

buildings. The red line represents the district heating network. The blue

line represents the cooling network connecting the ATC building with the

ATC conference room. From these view we can see that the project covers

six blocks. The energy production scheme will be presented later in detail.

3.3.2 Refurbishment of the ACT building

Figure 3.11 ATC building facade

The headquarter of the Agenzia Territoriale per la Casa della Provincia di

Turin (Housing Authority of the Province of Torino), ACT building is a 10

storey commercial building, build in the 1970s, with wide transparent

facades (see Figure 3.11). Different measures have been implemented to

reduce the energy demand (electrical, heating and cooling) of the building:

U-value of widows decreased from 3,8 W/(m2K) to less than 1,65

W/(m2K)

Insulation of walls and balconies has been carried out with panels

of mineralised wood fibres (thickness 25 – 35 mm, U-value of the

2,5 – 1,8 W/(m2K)) protected by a bituminous sheath.

The Efficiency of the climatisation was improved by the

installation of an adsorption chiller, thermally coupled with the gas

cogenerator.

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Cooling is generated with adsorption chiller using the heat

produced by cogeneration unit (Tri-generation)

Photovoltaic System of 50 kWp, connected directly to the low

voltage grid without any connection to the building electric power

system.

District Heating build ad hoc, before in the Arquata District there

was not heating or only inefficient small electrical and fossil fuelled

boilers.

Under typical condition, the co-generator plant works in parallel with the

local electric energy distribution network. The electric energy produced by

the cogenerator partly supplies the ATC building’s demand and partly is

sold to the National Managing Authority. Table 3.13 gathers the building

specification taken from reports [60].

There are some discrepancies between the data included in the report [60]

and the dates of the report [61]. We decided to use the first one because is

the latest and more complete.

Table 3.13 ATC building specification

Building characteristics Heated building volume 34050m3

Total envelope area 11350m2

Energy consumption [51] Total space heating 66,5 kWh/m2/yr

Electrical energy 90,32 kWh/m2/yr

Cooling 26 kWh/m2/yr

Technical equipment Combined Heat and Power

(CHP)

1 MWe, 1,2 MWt

Three gas boiler 2 with 1300 kW each one

1 with 978 kW power

Photovoltaic system 50 kWp

Adsorption chiller 190 kWc, thermally

coupled with the gas

cogenerator

The technical building equipment characteristics, in particular CHP and

photovoltaic system are presented in more detail below.

The ATC building PV plant has an overall peak power of 49,14 kW. The

234 polycrystalline silicon PV modules are installed on the southwest

facade (34,14 kW) and on the southeast facade (15 kW) (see Table 3.14).

The PV modules were installed above the windows of the building (Figure

3.12), so that they can simultaneously satisfy two purposes, namely

production of electrical energy and shading of the offices (with an expected

saving on summer cooling of the building), in fact, direct solar irradiance

strikes almost entirely these two facades during the hottest hours of

summer days. The installation of PV panels on the two facades result in a

3 The CONCERTO project experience

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shading effect for the offices behind them. Different tilt angles and panels’

positioning have been compared to give the best energetic performance

taking into account corresponding production of electric energy [58].

Table 3.14 ATC building PV generators

Global characteristics Nominal Power 49,14 kWp

Number of modules 234

Power ratio 1,14

Expected performance ratio 91%

Performance results [51] Yearly production 54,832 MWh/yr

Figure 3.12 ATC PV system

3.3.3 Refurbishment of social housing buildings

Figure 3.13 Social housing buildings

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The retrofitting of Arquata buildings is subjected to several constraints,

particularly on the decorated facades, in order to preserve their

architectural value; the building were, in fact, built at the beginning of the

XX century (see Figure 3.13). Different retrofit actions have been

implemented to reduce the energy consumption of the blocks while

keeping equal or increasing the environmental quality for the inhabitants.

The interventions were:

500 conventional windows have been replaced with low emissivity

glazing and window frame with thermal break (i.e. the window

thermal transmittance passed from U = 4 W/(m2K) to U = 1,6

(m2K)).

120 kW PV system have been installed on the roofs of 12 district

buildings (see Figure 3.14).

214 water flow meter have been installed.

Connection to the district heating network

Table 3.15 shows the characteristic of the social house buildings.

Table 3.15 Social housing buildings specification

Building characteristics Heated building volume 99444m3

Total envelope net area 29855m2

Total envelope gross area 35827m3

Energy Consumption [51] Heating 95 kWh/(m2yr)

Electrical energy 32,24 kWh/m2

Technical equipment Photovoltaic plants 120 kWp on 12 district

buildings

214 Water flow meters 0,1

bar

District heating network

For the PV system the original POLYCITY project expected a capacity of

100kW; thus the power has been increased by around 20%. Each plant is

made with 52 modules of 210 W each, other characteristics are presented

in Table 3.16 . For every building usually 3 inverters are installed. The PV

production is used to supply the common loads of the buildings. The

surplus of energy flows toward the network and, when the production is

not enough to supply the load, the missing power is adsorbed by the

network.

Table 3.16 Social house buildings PV generators

Global characteristics Nominal Power 130,92 kWp

Number of modules 624

Power ratio 1,07

Expected performance ratio 91%

Performance results [51] Yearly production 173,978 MWh/yr

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The yearly production includes the power of all the 12 PV systems. The

yearly production of each 12 PV systems is reported in Figure 3.15.

Figure 3.14 Social house building PV system

Figure 3.15 Residential building’s PV yearly production residential building

3.3.4 District heating and cogeneration

A district heating network has been realized in order to supply space

heating and sanitary hot water to the residential buildings as well as to the

ATC building. Bulk heat is supplied by a natural gas cogenerator with

characteristics gathered in Table 3.17. The peak demand is complemented

by three high efficiency boilers. All the equipment was installed in the

underground floor of the ATC building.

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At the beginning of 2008 a total amount of 489 dwellings were already

served, corresponding to a heated volume of 73000 m3. In every single

dwelling a satellite control module manages through a valve the supply

flow, according to an environmental thermostat for space heating.

Table 3.17 CHP main features

Engine DUETZ TCG 2020 V12 K

Electrical power 968 kW

Thermal power coming from hot water recovery on the

engine block

474 kW

Thermal power coming from hot exhaust gas recovery 692 kW

Overall efficiency 0,85

ηth (guaranteed minimum at full load in ISO 3046

conditions)

0,464

ηel (guaranteed minimum at full load in ISO 3046

conditions)

0,386

CHP gas consumption (m3) (2008-2009) 947631,50

CHP electric production (MWh) (2008-2009) 3506,490

CHP thermal production (MWh) (2008-2009) 3465,692

The energy concept in the Arquata project is synthesized in the scheme of

Figure 3.16.

Figure 3.16 Arquata energetic and metering system [62]

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3.3.5 Impact on the Arquata district

A substantial impact is expected from the POLYCITY project with respect

to the previous situation both in terms of primary energy saving and of CO2

emission reduction (see Table 3.18).

The POLYCITY project is expected also to produce economic and social

benefits at different levels: in energy costs ( reduction of 30-40% respect to

initial situation), real estate value increase due to the efficiency

improvements, improved quality of life and services for the inhabitants,

information and education regarding sustainable services and consuming

behaviours.

Table 3.18 Sustainability impact of the project, calculated value [53]

Impact Saving %

Primary Energy -7786 MWh/yr -43

CO2Emissions -1967 t CO2/yr -52

3.4 Analysis of CONCERTO database

The Arquata project may be further analysed by using the CONCERTO

database, as shown in Figure 3.17.

One of the first issue of the database is that the refurbished building may

be hardly distinguished, so it is difficult to identify and separate the ACT

building from the other social housing buildings.

The same problem is also present for the ESUs which are hardly

distinguishable.

For example, 13 different photovoltaic units are installed in the Arquata

project and it is impossible, using only the database, to know what are the

12 installed on the social house buildings and what is the one installed on

the ATC building.

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Figure 3.17 Screen of CONCERTO Database

By crossing data available on reports and the database is nevertheless

possible to identify the technical indicators available in the database, for

the ACT building. They are: the final energy demand for space cooling and

space heating and the fossil/renewable primary energy demand for space

cooling and heating both based design and monitoring data (Table 3.19

reports the monitoring data).

Table 3.19 CONCERTO Database technical indicators for ATC building

Final energy demand for space cooling 29,264 kWh/(m2yr)

Final energy demand for space heating 47,818 kWh/(m2yr)

Fossil primary energy demand for space cooling 27,837 kWh/(m2yr)

Fossil primary energy demand for space heating 66,156 kWh/(m2yr)

Unfortunately the CONCERTO Database doesn’t provide information

about the electric consumption of the building.

It worth noting that that, the fossil primary energy demand for space

cooling is lower than the final energy demand. This is due to the COP of

the adsorption chiller which is greater than one, which brings an advantage

in terms of primary energy expenditures.

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Analogous indicators are reported for the 12 social housing building all

together (Table 3.20) The final energy demand indicators are based on

monitoring data while the other are based on design data. However these

data does not see reliable because domestic hot water and space can hardly

have the same value in a real building.

Table 3.20 CONCERTO Database technical indicators for social housing building

Final energy demand for domestic hot water 58,332 kWh/(m2yr)

Final energy demand for space heating 58,332 kWh/(m2yr)

Final energy demand for electricity 47,818 kWh/(m2yr)

Fossil primary energy demand for domestic hot water 68,740 kWh/(m2yr)

Fossil primary energy demand for space heating 68,740 kWh/(m2yr)

Fossil primary energy demand for electricity 0,018 kWh/(m2yr)

Analogous problems are also present in the environmental indicators

(Table 3.21)

Table 3.21 CONCERTO Database environmental indicators for social housing

building

CO2 emissions for domestic hot water 13,466 kg/(m2yr)

CO2 emissions for space heating 13,466 kg/(m2yr)

CO2 emissions for electricity 0,004 kg/(m2yr)

Another problem of the database is that many indicators are not available

for the buildings such as the reduction of energy use and the reduction of

emissions.

This is a serious lack of the Database, because is essential, for a complete

energy analysis, compare the situation before and after the refurbishment.

The district network was one of the most important improvement in

Arquata district, nevertheless indicators are not available for it.

The other ESU are named as Other 1 and as Other 2. They probably are

the boilers or the adsorption chiller, but there is no evidence of this.

The most significant problem for the CHP units concerns the Efficiency

indicator. In fact, the efficiency of the CHP is 477.603 instead of 0.85.

This is a common problem in CONCERTO Database because, if efficiency

indicators are available for the 94.3 % of the projects, their values are often

wrong. For example, always in Arquata project, the 13 photovoltaic

systems present in the Database have an efficiency of 1.

Regarding the energy production of CHP we obtain the values present in

Table 3.22.

Table 3.22 Energy output CHP

Annual electricity output 3371 MWh/yr

Annual heat output 3185 MWh/yr

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Also the Final Energy Demand and Fossil Primary Energy Demand

indicators present several problems.

The Final Energy Demand of CHP is divided in final energy demand for

cold, for electricity and for heat (see Figure 3.18). From the Figure we can

notice that the energy demand for heating is only 40 MWh/a. This result

contrasts with the Annual Heat output which is 3371 MWh/a. So the

Energy Demand for Heating indicator is wrong, considering that, according

to the definitions of the indicator, the energy demand corresponds to the

energy entering the energy supply unit, and therefore will be always

greater than the energy output when the efficiency is lower than one, as the

case of CHP.

At the same time, is also incorrect attribute to CHP the cold final energy

demand because it is supplied to the building by the adsorption chiller.

Then observing the Final Energy Demand for different ESU on the same

graph (Figure 3.19), it is clear that the database associates each ESU the

same value of these indicators.

Figure 3.18 Final Energy Demand for CHP

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Figure 3. 19 Final Energy Demand of Different ESU

For the Photovoltaic Annual electricity output (Table 3.23), The

CONCERTO database indicator presents no problem, and the values are

comparable to those of Table 3.14 And Table 3.16

Table 3.23 Annual electricity output of all PV units

Photovoltaic 1_1 annual electricity output 64,1 MWh/a

Photovoltaic from 2 to 13 Total annual electricity output 163,24 MWh/a

For ESU’s environmental indicators, the CO2 Emission Reduction based

on monitoring data is available (see Table 3.24). The Total CO2 emissions

reduction are equal to 1517.42 ton/a, a similar value to that of Table 3.18.

Table 3.24 𝑪𝑶𝟐 emissions reduction for Arquata ESU

CHP CO2 emissions reduction 1478584,492 kg/a

Photovoltaic 1_1 CO2 emissions reduction 2314,232 kg/a

Photovoltaic 2 CO2 emissions reduction 1037,596 kg/a

Photovoltaic 3 CO2 emissions reduction 4297,283 kg/a

Photovoltaic 4 CO2 emissions reduction 783,895 kg/a

Photovoltaic 5 CO2 emissions reduction 783,895 kg/a

Photovoltaic 6 CO2 emissions reduction 4242,406 kg/a

Photovoltaic 7 CO2 emissions reduction 4271,533 kg/a

Photovoltaic 8 CO2 emissions reduction 2471,993 kg/a

Photovoltaic 9 CO2 emissions reduction 847,637 kg/a

Photovoltaic 10 CO2 emissions reduction 1156,214 kg/a

Photovoltaic 11 CO2 emissions reduction 2824,050 kg/a

Photovoltaic 12 CO2 emissions reduction 2631,558 kg/a

Photovoltaic 13 CO2 emissions reduction 3358,466 kg/a

Total 𝐂𝐎𝟐 emissions reduction 1517,42 ton/a

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3.5 Discussion

As we have seen the CONCERTO technical monitoring database is an

analysis tool rather incomplete.

Indicators are often not available or wrong. These indicators, if used for

further analysis could lead to substantial errors.

Finally, The Concerto database does not provide indication about the

Smart City, but only about some buildings and some ESU installed in a

neighbourhood. An overview on the entire neighbourhood, in fact, is not

included.

In the following Table 3.25 the practical and content lacks of the database

are reported as summary analysis.

Table 3.25 Practical and content lacks CONCERTO Technical Monitoring Database

Content Lacks Pratical Lacks Indicators are available only at

building and systems scale.

No information about the

neighbourhood or the city

Many errors are reported for the

energy efficiency indicator.

The final energy demand for ESU is

impossible to use.

Errors are reported also for Building

indicators.

Indicators availability is often lower than

50%, especially for cooling indicators.

May problems are reported in the

identification and choice of the

indicators.

Graphical representations of the

indicators is sometimes chaotic.

In the presence of many buildings or

ESUs, it is difficult to immediately

distinguish between them.

Lack of care Database compiling

Files download from the database results

incomplete

84

4 The effect of automatic control on building energy need/use

85

4 The effect of automatic control on

building energy need/use

4.1 Automatic control in buildings

Building automation and control systems (BACS) provide automatic

control of the conditions of indoor environments and are the core of the so

called smart building. The historical root and still core domain of BACS is

the automation of heating, ventilation, air-conditioning (HVAC) systems,

lighting and shading. In large functional buildings their primary goal is to

realize significant savings in energy and reduce cost [63-65].

Wolfgang Kastner et al. [66] describe the evolution of the building

automation during the years. Building automation (BA) is concerned with

the control of building services. Initially, controllers were based on

pneumatics. These were replaced by electric and analog electronic circuits.

Finally, microprocessors were included in the control loop. This concept

was called direct digital control (DDC), a term which is still widely used

for programmable logic controllers intended for building automation

purposes. The oil price shock of the early 1970s triggered interest in the

energy savings potential of automated systems, whereas only comfort

criteria had been considered before. As a consequence, the term “energy

management system” (EMS) appeared, which highlights automation

functionality related to power-saving operation. Further, supervisory

control and data acquisition (SCADA) systems for buildings, referred to as

central control and monitoring systems (CCMS) were introduced. They

extended the operator’s reach from having to handle each piece of

equipment locally over a whole building or complex, allowing the

detection of abnormal conditions without being on-site. Also, the service of

accumulating historical operational data was added. This aids in assessing

the cost of operation and in scheduling maintenance. Trend logs provide

valuable information for improving control strategies as well. Often, BA

systems with these capabilities were referred to as building management

systems (BMS).

Today’s comprehensive automation systems generally go by the all-

encompassing name of BAS, although EEMS building, and BMS are still

in use, sometimes intentionally to refer to specific functional aspects.

Figure 4.1 illustrates these different dimensions. The international

standard EN 15232 chooses building automation and control systems

(BACS) as an umbrella term.

4 The effect of automatic control on building energy need/use

86

Figure 4.1 Functional aspects of building automation systems (BAS) [66]

For Jon Höller et al. [63] a BAS consist of the following components (see

Figure 4.2):

Sensor (i.e. devices that measure, such as thermometers, motion

sensors, and air pressure sensors)

Actuators (i.e. controllable devices, such as power switches,

thermostats, and valves)

Programmable logic controllers (PLCs) that can handle multiple

inputs and outputs in real time and perform regulating functions.

A server which monitors and automatically adjusts the parameters

of the system while allowing an operator to observe and perform

supervisory control.

One or more network buses

To design a home automation system simplified wiring is used; one cable

connects all the devices. The cable is the vehicle of communication,

wherewith the devices interact with others exchanging data and

information. The cable is usually defined “bus”.

Most building automation networks consist of a primary and secondary

bus which connect high-level controllers with lower-level controllers,

input/output devices and a user interface.

There are several possible bus configuration, the only limitation is the

maximum number of inserted devices and the maximum length that we

must respect in relation to the used cable type as described by Scuola Edile

Bresciana [67].

4 The effect of automatic control on building energy need/use

87

The exchange of information takes place according to standard

communication protocol. A communication protocol can be described as

the language that direct digital control modules use to communicate each

other.

the most popular standard are: Batibus, CEBus, Konnex, LonWorks, the

ASHRAE’s open protocol BACnet or the open protocol Lon Talk [64;66].

LonWorks, BACnet, and CEBus were designed specifically for the data

transmission needs of a control network. All three are open standards,

meaning that any manufacturer of direct digital control components can

use them, and if they follow the standard closely, their products should be

compatible with any others using the same protocol. By contrast, many

building automation system manufacturers use proprietary communications

protocols such as Metasys and MicroTech. These can only be used in

components from licensed manufacturers, which helps assure compatibility

of components but limits product selection.

Analog inputs are used to read a variable measurement. Examples are

temperature, humidity and pressure sensor which could be thermistor or

platinum resistance thermometer, or wireless sensor. Analog outputs

control the speed or position of a device. An example is a hot water valve

opening up 25% to maintain a set-point. Digital output are used to open

and close relays and switches as well as drive a load upon command.

Different types of command and actuators are describe by Scuola Edile

Bresciana [67] we have: On/Off, dimmer light, up/down for blinds etc.

Figure 4.2 Configuration for a BAS [63]

4 The effect of automatic control on building energy need/use

88

Besides the immediate savings, indirect benefits may be expected due to

higher expected workface productivity or by the increased perceived value

of the automated building. Although investment in building automation

systems will result in higher construction cost, their use is mostly

economically feasible as soon as the entire building life cycle is

considered.

Standard EN 15232 “Energy Efficiency in Buildings – Influence of

Building Automation and Control and Building Management” was

introduced to take advantage of energy savings potential of control and

building operation. The standard makes it possible to identify the savings

potential from building automation and control and then to derive measures

to improve energy efficiency [68].

In particular four different BACS efficiency classes (A, B, C, D) of

functions are defined both for non-residential and residential buildings:

Class D corresponds to non-energy efficient BACS. Building with

such systems shall be retrofitted. New buildings shall not be built

with such systems

Class D corresponds to standard BACS

Class B corresponds to advanced BACS and some specific

technical building management functions

Class A corresponds to high energy performance BACS and

technical building management

In addition 2 calculation methods (a detailed one and a simplified one) to

estimate the impact of the automation system on buildings energy

efficiency are proposed.

The simplified method is called BACS factor method. The BACS factor

method has been established to allow a simple calculation of the impact of

building automation, control and management functions on the building

energy performance. The BACS factor method gives a rough estimation of

the impact of BACS on thermal and electric energy demand of the building

according to the efficiency classes A,B,C and D. The BACS factor method

is specially appropriated to the early design stage of a building.

The norm uses four sets of BACS efficiency factor:

Thermal energy for space heating and cooling (𝑓𝐵𝐴𝐶𝑆,ℎ - 𝑓𝐵𝐴𝐶𝑆,𝑐)

Thermal energy for domestic hot water generation (𝑓𝐵𝐴𝐶𝑆,𝐷𝐻𝑊)

Electric energy for ventilation, lighting and auxiliary devices

(𝑓𝐵𝐴𝐶𝑆,𝑒𝑙)

The whole calculation sequence of the BACS efficiency factor method is

depicted in Figure 4.3. As to be seen one of the BACS efficiency classes

shall be defined as a reference case first. Normally class C which

4 The effect of automatic control on building energy need/use

89

corresponds to a state of the art building automation and control system is

set as reference case. For this reference case, the annual energy use of the

building energy system shall be calculated. The BACS factors the allow to

easily asses the energy performance of a building operating with a building

automation and control system different to that system defined as the

reference case. Since the relevant efficiency factors have to be set in

relation against each other also building energy performance is in relation

to a reference case

Figure 4.3 Calculation sequence of BACS efficiency factor method

4.1.1 Heating/Cooling systems

As we previously said space heating and cooling in buildings accounts for

an increasingly large proportion of the energy consumption of the building

sector. For example the most important energy end-use in the building

sector in the UK is space heating which accounts for over 60% of delivered

energy and over 40% of energy costs [69]. The indoor air temperature

usually serves as an index to represent the thermal comfort. In cold

weather, comfortable indoor temperatures can only be maintained by the

heating devices which provide heat to the space at the same rate as the

space is losing heat. Similarly, in hot weather, heat should be removed

from the space at the same rate that it is gaining heat. The rate at which

4 The effect of automatic control on building energy need/use

90

heat is gained or lost is a function of the differences between the inside air

temperature and the outside air temperature. Therefore, in order to

maintain a stable thermal comfort, the heat balance that determines the

indoor temperature needs to be properly controlled by heating and cooling

devices [69-70]. The control of the heating/cooling system could be done

on the different parts of which the system is composed of: the heating

generation (boilers, heat pumps, chillers), heating distribution and heating

consumers (emitter control: radiators, floor heating, fun coils) [71-74] (see

Figure 4.4).

Figure 4.4 Synthetic scheme for heating/cooling system [68]

Siemens [68] reports all the heating and cooling automated control

systems, below we report the main control system.

Individual room control using thermostatic valves or with electronic

controller (heating consumers): the thermostatic radiator valve is a

mechanical temperature controller without auxiliary energy that supplies

depending on the actual room temperature a radiator via a valve with lower

or higher heating water flow to maintain a constant room temperature. The

temperature sensor consists of one expansion element filled with gas or

liquid. The element expands as the room temperature increases. The valve

closes slowly and lowers in this manner water flow and the radiator

radiates less heat. The function minimizes energy use and permits different

temperatures in separate rooms and can be used to control: radiators and

floor heating for thermostatic valves and fun coils.

A scheduler integrated on the controller allows for time controlled

operation of a room using temperatures dependent on occupancy, such as:

Scheduler programs allow energy reduction during non-occupancy

periods

Demand control with presence detector

Setback of temperature for absences (holidays, working hours, etc.)

Night setback

Comfort mode for occupancy

Heating/Cooling generation

Heating/cooling distribution Heating/Cooling consumers

4 The effect of automatic control on building energy need/use

91

This type of control is studied by Ryan Naughton et al. [71], , in their study

the control is applied on a radiator, and it brings a substantial saving on the

gas expenditure for heating (between 12% and 29%).

Peeters et al. [74] say that 80% of the heating system have an emitter

control with thermostatic valves on the radiators.

Individual room control with demand control (heating/cooling consumers):

Individual room control is primarily responsible for controlling room

temperature. A temperature sensor is used to record the actual value for

room temperature; it is compared with the set point and calculated heating

water flow is controlled via a positioning unit. The individual room

controller further processes all user interventions (set-point, operating

state) and room demands (room occupancy with presence detector, window

contact, disturbance variables) and determines the heating demand. The

demand information is transmitted to heating distribution and generation

control for processing. Where it is used to determine whether the plant

components must be switched on and the temperature level for consumers.

An adjustable maximum limitation of room temperature helps prevent

excessive heating during control override.

A scheduler is available for each controller or room group for time-

controlled operation of a room using temperatures dependent on

occupancy. The function can be used to control: radiators, convectors, floor

heating, electric direct heating, fun coils and chilled ceilings.

Outside air temperature dependent supply temperature control

(heating/cooling distribution): The required heat/cooling output for a

building zone is determined using the outside air temperature value for

outside air temperature dependent control. A sensor attached to the

building’s exterior shell records outside air temperature; the required

supply temperature is the calculated per this measured value. This

relationship provides the so-called heating and cooling curve (see Figure

4.5).

The function is also referred to as weather dependent supply temperature

control. At the given radiator size, output can be changed by adapting the

temperature of the heating water (supply temperature), for cooling surfaces

the cooling output is proportional to the temperature difference between

room temperature and average cooling temperature.

Further considered are foreign energy sources inside that are not recorded

by an outside sensor. This disadvantage cam be minimized using a

temperature sensor in the reference room that influences the supply

temperature. The function is used in heating and cooling control for

radiators, convectors, floor heating and fun coils.

4 The effect of automatic control on building energy need/use

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Figure 4.5 Heating curve [68]

Heating/Cooling demand signal (heating/cooling distribution): The

relevant information on all rooms belonging to a heating/cooling circuit is

collected. The values may consist of valve settings, room temperature or

operating states. The demand signals from multiple sources are gathered,

evaluated and mapped to resulting heating demand signals. The valves and

pumps are controlled based on these demand signals; the demand signals

for heating generation are determined. The function reduces energy use and

it lowers operating hours of the circulating pumps and heating and cooling

loss in the piping network.

Control of pumps (heating/cooling distribution/): The pressure differential

increases in the piping network as valves for heating consumers close due

to lower heating demand. A pressure sensor records the change as a

differential measurement. This pressure differential is maintained at a

constant level by controlling volume flow rate with the help of a variable

speed drive. Maintaining the pressure differential over the pump at a

constant level already reduces the power consumption. You can also

reduce output by using multi-stage control of the circulating pump. The

function reduces power consumption. The pumps could have on/off

control, multi-stage control or variable speed pump control, depending

from the pumps type.

Generator temperature control by outside air temperature (heating/cooling

generation): The problem associated with the control of generator in

heating/cooling systems has been rarely mentioned in the literature [69].

Only 6% of the boilers are controlled by an external temperature

compensated scheme [74]. In principle, the goal is to lower the operating

temperature at generation as much as possible. For heat pumps, the output

4 The effect of automatic control on building energy need/use

93

number and yearly energy efficiency ratio increases due to the smaller

difference between condensation and evaporation temperature. For chillers,

the coefficient of performance (COP) and yearly energy efficiency ratio

increases due to the smaller difference between evaporation and

condensation temperature. In contrast to generating domestic hot water,

heat and cooling demand from the rooms responds in a quasi linear manner

to the outside air temperature. As a result, the supply temperature at

generation should be controlled by outside air temperature to save energy.

The function improves the yearly energy efficiency ratio at generation. It

lowers: boilers losses, heat loss in piping network and runtime of burners,

pumps and compressors.

Generator temperature control based on load (heating/cooling

generation): Heating and cooling demand for all consumers and manual

set-point specifications are collected. The maximum for heating and

minimum for cooling value is derived from these set-points. This value

represents the actual load conditions and is used as set-point for generation

supply temperature. The risk exists when using a maximum or minimum

selection for a single room as the temperature level, that this temperature is

too high or too low for other consumers. Using the average of multiple

rooms to determine the temperature level ensures that all consumers can

cover the heat demand. Limiting generation temperature to an adjustable

value can prevent supply temperatures that are too high or too low and this

optimizes energy efficiency, The function improves the yearly energy

efficiency ratio of the generation. It lowers: losses from standstill, standby

losses at boiler plants, heating and cooling loss in piping network, runtime

of burners, pumps and compressors.

Table 4.1 shows a summary situation gives by norm EN 15232 about

automatic control for heating and cooling system.

Table 4.1 Heating/Cooling automatic control in buildings: summary table Norm EN

15232

Heating/Cooling control

1.1 Emission control

0 No automatic control of the room temperature

1 Central automatic control

There is only central automatic control acting either on the distribution or on the

generation. This can be achieved for example by an outside temperature controller

conforming to EN 12098-1 or EN 12098-3

2 Individual room control

By thermostatic valves or electronic controller ( supply output based on room

temperature “controlled variable”. It consider heat/cooling sources in the room as well.

The room can be kept comfortable with less energy

3 Individual room control with communication

Between controllers and building automation and control systems (same as above in

4 The effect of automatic control on building energy need/use

94

addition central schedulers make it possible to reduce output during non-occupancy)

4 Individual room control with communication and presence control

Between controllers and BACS; Demand/Presence control performed by occupancy

1.2 Control of distribution network hot/cold water temperature (supply or return)

Similar function can be applied to the control of direct electric heating networks

0 No automation control

1 Outside temperature-compensated control

Action lower the mean flow temperature (distribution temperature is controlled

depending on the outside. This reduces energy losses under part load conditions.)

2 Demand-based control

E.g. based on indoor temperature; actions generally lead to a decrease of the flow rate.

1.3 Control of distribution pumps in networks

The controlled pumps can be installed at a different levels in the network

0 No automation control

1 On/off control

To reduce the auxiliary energy demand of the pumps (electrical power of the pump is

drawn only as required e.g. during occupancy periods or in protection mode

2 Multi-stage control

To reduce the auxiliary energy demand of the pumps (operating at a lower speed

reduces power consumption of multi-speed pumps)

3 Variable speed pump control

With constant or variable Δp and with demand evaluation to reduce the auxiliary energy

demand of the pumps

1.4 Intermittent control of emission and/or distribution

0 No automation control

1 Automation control with fixed time program

To reduce the indoor temperature and the operation time

2 Automatic control with optimum start/stop

To reduce the indoor temperature and the operation time

3 Automatic control with demand evaluation

To reduce the indoor temperature and the operation time

1.5 Generator control for combustion and district heating

0 Constant temperature control

1 Variable temperature control depending on outdoor temperature

(generation temperature is controlled depending on the outside temperature)

2 Variable temperature control depending on the load

1.6 Generator control for heat pumps

0 Constant temperature control

1 Variable temperature control depending on outdoor temperature

Generation temperature is controlled depending on the effective temperature demand of

the consumers, keeping the COP at an optimum

2 Variable temperature control depending on the load

4 The effect of automatic control on building energy need/use

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1.7 Sequencing of different generators

0 Priorities only based on running time

1 Priorities only based on load

2 Priorities based on loads and demand of the generator capacities

3 Priorities based on generator efficiency

The generator operational control is set individually to available generators so that they

operate with an overall high degree of efficiency

4.1.2 Lighting Systems

Lighting represent a significant portion of the total electricity consumption

of all building types, and it is more prominent in commercial buildings. For

example according to US Department of Energy, lighting load represent

14% energy consumption in commercial buildings on average. A European

study shows that in case of medium and large buildings, about 40% of the

total electricity is used for interior lighting [75). So reduction in lighting

load can have significant positive impact in decreasing the electricity

demand of the buildings.

On/Off controls and lighting reduction controls are manual controls that

are needed in most spaces. However there is no guarantee that these

controls will save energy, because they rely on occupants behaviour in

order to obtain energy savings.

Automatic lighting controls instead guarantee energy savings from

lighting. The automatic shutoff can be implemented by a single device or

by multiple devices, the most recurrent solutions for lighting systems

found in the literature [64][75-79] are:

Occupancy-based control

Lighting control by time scheduling

Daylight-linked controls

Mixed control system

Occupancy-based control: Among the control schemes used for lighting

automation occupancy sensor technologies have been used for a long time,

occupancy sensors employ some sort of motion sensing technique to detect

the presence of occupants in a given range of space, so the lights are

switch on when it detects any occupant, and switched off when there is no

occupant within a pre-fixed delay period (Standard EN 15232 set it as 5

minutes, instead ASHRAE Standard 90.1 set the limit to 30 minutes after

all occupants have left the space [78]. The technology of the sensor can be

of different types and cost: Passive Infrared (PIR), Ultrasonic, Acoustic,

Microwave type are currently in use [75].

4 The effect of automatic control on building energy need/use

96

Mohammad Asif ul Haq et al. [75], studied the energy savings obtained by

applying occupancy based controls in different cases study, the result are

presented in Figure 4.6.

Figure 4.6 Savings from occupancy based controls [75]

Moreover, based on these studies, the Time Delay, sensitivity of sensors

and positioning and coverage zone of sensor influence the performance

obtained by the control. The effects of Time Delay, sensitivity and

coverage area on the overall performance of occupancy based lighting

control systems are summarized in Table 4.2

Table 4.2 Effects of different parameters on occupancy control performance [75]

Parameter Too high Too low

Time delay Less savings Reduced lamp life due to frequent

switching. Possible user

dissatisfaction

Sensitivity “False On” – detecting false

movements coming from

sources other than occupants,

thus keeping light on.

“False Off” – failure to detect

occupants thus turning light off

despite presence, resulting in user

dissatisfaction as well as

unnecessary switching.

Coverage area Too large

Detection of movement from

adjacent space through

doors/windows, thus keeping

lights on unnecessarily.

Too small

Results in undetected zones in the

workspace, where occupants are not

detected despite presence.

Lighting control by time scheduling: Lighting control systems based on

scheduling operate on very simple principle based on fixing an operating

time of the light fixtures. The lights which are controlled by the control

system are switched on and off based on a pre-fixed schedule. Scheduling

systems are based on time, so it is useful in areas where the occupancy

pattern is accurately predictable. For instance, a classroom may have a

fixed routine to hold classes from 9:00 AM to 2:00 PM and then after a 1

hour break the classes resume from 3:00 PM to 5:00 PM. In such a

classroom, a simple time switch may be used to turn the light system on

4 The effect of automatic control on building energy need/use

97

during the time when the classes are scheduled to be held and turn the

lights off during lunch break and after class hours.

Properly commissioned time-based control systems can provide substantial

savings Rubinstein et al. [80] reported savings in office building

applications between 10% and 40%. Scheduling systems are commonly

used in combination with other control systems like occupancy sensors and

daylight control as well.

Daylight-linked lighting controls: Daylighting is a means of bringing

natural light into a space to provide comfort and a connection to the

outdoors. It has many benefits including the ability to provide a better

indoor environment as well as save energy by replacing electric lighting.

Daylight controls are based on the use of photocells that sense the amount

of light reflecting off a daylighted surface or the intensity of light coming

through an opening such as a window. A photocell sends a signal to a

controller indicating the light level in the space. The controller adjusts the

electric lighting output through direct dimming or switching. The photocell

and controller must be calibrated to desired illuminance levels prior to use

[78].

For example, in a school gym, an illuminance level of 300 Lux may be

required at the floor level. The daylight dimming system must be calibrated

such that when 150 Lux of daylight is received at the floor level, light

output of the overhead luminaires is reduced by half by the daylight

dimming system.

The human impact of daylight in workspaces is also an important factor to

consider. Visual comfort is a key factor in increasing overall quality of life

inside any building. Apart from providing energy savings by reducing

lighting load, presence of daylight has been proven to boost productivity

and visual comfort [63]. But direct sunlight entrance or reflection from

surfaces can create glare, causing discomfort. Glare is a sensation that

occurs when the luminance level of the visual field is higher than the

luminance level human eyes are adapted to. To counter this problem, some

daylight control systems include automated window blinds to maintain

appropriate amount of daylight entrance to ensure lighting as well as visual

comfort by reducing daylight glare [78]. Excessive daylight entrance may

also increase the heating of the room, thus increase cooling load for the air

conditioners. Similarly, large window areas will allow more heat loss in

cold weather.

Daylight-linked lighting controls can be divided into two types based on

how they control the lighting system: Daylight-linked Switching and

Daylight-linked Dimming. Daylight-linked switching can control the lights

by switching On and Off states based on available daylight. Table 4.3

shows the advantages and disadvantages of the two types of control.

4 The effect of automatic control on building energy need/use

98

Table 4.3 Comparison between daylight-linked switching and dimming controls [75]

Factors Switching Dimming

Advantages High savings in suitable areas

Low initial cost compared to

dimmable systems

Relatively easy installation

High savings in variable daylight

Gradual change between light levels,

thus less obtrusive to occupants

Greater accuracy in control

Disadvantages Less accuracy in control

Prominent change in lighting

state causes less user

acceptance

Higher initial cost

Request precise tuning for optimum

performance

Always Mohammad Asif ul Haq et al. [75], studied the energy savings

potential of daylight-linked lighting control schemes. These studies vary

based on the type or room where the control is implemented. Savings

reported from such studies are presented in Figure 4.7.

Figure 4.7 Savings from daylight linked controls [75]

Mixed control system: As shown in the previous discussion each of these

technologies has their unique characteristic. A particular control scheme

may give better performance in a certain scenario respect another situation.

These technologies often fail to provide satisfactory performance due the

shortcomings associated with that particular technology. In order to

overcome these disadvantages and ensure maximum amount of savings

without compromising user satisfaction, researchers have experimented

with combinations of multiple types of control schemes in one systems. It

has been seen that combining technologies together gives substantial

improvements in terms of accuracy and energy saving [75].

Table 4.4 shows a summary situation gives by norm EN 15232 about

automatic control for lighting.

Table 4.4 Lighting automatic control in buildings: summary table Norm EN 15232

Lighting Automatic Control

1.1 Occupancy Control

4 The effect of automatic control on building energy need/use

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0 Manual on/off switch

The luminary is switched on and off with a manual switch in the room

1 Manual on/off switch + additional sweeping extinction signal

The luminary is switched on and off with a manual switch in the room. In addition, an

automatic signal automatically switches of the luminary at least once a day. Typically in

the evening to avoid needless operation during the night

2 Automatic detection

Auto On/Dimmed Off: The control systems switches the luminary automatically on

whenever there is presence in the illuminated area, and automatically switches them to a

state with reduced light output no later than 5 min after the last presence in the

illuminated area. In addition no later than 5 min after the last presence in the room as a

whole is detected the luminary is automatically and fully switched off

Auto On/ Auto Off: The control system switches the luminary automatically on

whenever there is presence in the illuminated area, and automatically switches them

entirely off no later than 5 min after the last presence is detected in the illuminated area

Manual On/Dimmed: The luminary can only be switched on by means of a manual

switch in the area illuminated by luminary and if not switched off manually they follow

the first case of section 2.

Manual On/ Auto Off: The luminary can only be switched on by means of a manual

switch in the area illuminated by the luminary, and if not switched off manually, is

automatically and entirely switched off by the automatic control system no later than 5

min after the last presence is detected in the illuminated area

1.2 Daylight control

0 Manual

There is no automatic control to take daylight into account

1 Automatic

An automatic system takes daylight into account in relation to automatisms described in

1.1

4.1.3 Ventilation System

In a building there is another kind of comfort in addition to the thermal

comfort, the air quality comfort.

The quality of the indoor air depends upon a number of factors including

the concentrations of a variety of gaseous and particulate pollutants in the

indoor air in particular the carbon dioxide (𝐶𝑂2). These air pollutants may

enter the building with outside air or may be generated internally. Outside

air is usually the dominant pollutant removal process. Using ventilation to

dilute contaminants, filtration, and source control are the primary methods

for improving indoor air quality in most buildings comes from the

inhabitants and other pollutant sources in the building. [70]

Indoor air quality is therefore influenced by two major components: the

amount and quality of outdoor air getting in, and indoor sources of

emissions. The influence of outdoor air quality on indoor air quality

depends on the air exchange rate. Inadequate air exchange rate causes poor

indoor air quality. On the other hand, too much outdoor air results in

energy waste [81].

4 The effect of automatic control on building energy need/use

100

The dynamic performance of a ventilation system has great impact on

indoor air quality as well as on power and energy consumption. Therefore,

reliable and optimal monitoring control of ventilation system are essential

to maintain adequate indoor air quality with least energy consumption [82].

The most used method to control the ventilation systems is to use an air

flow control at the room level.

The most simply method is to use a time control. Then the systems runs

according to a given time schedule. Scheduled ventilation can work

effectively with classrooms or scheduled meeting rooms. In this system,

the occupancy is estimated based on a class or rental schedule and this

information is input into the control system. To be effective, the system

requires ingoing entry of schedule information or integration with a

scheduling calendar system [83].

Another control is to use Occupancy sensing using occupancy sensors to

detect if anyone is in the space or if the space is vacant. For occupancy

sensing, either full ventilation is provided, so full ventilation will be

provided whether there is one person in the space or the space is fully

occupied.

For efficient control of the indoor air quality, demand-controlled

ventilation (DCV) systems are deployed to reduce the energy consumption

and improve the indoor air quality. A demand-controlled ventilation

system decides the amount of outside air brought into the building

according to occupants need. It adjust the amount of outside air based on

the number of the occupants and the ventilation demands from the

occupants [82]. These systems have a 𝐶𝑂2 sensor in each space or in the

return air and adjust the ventilation based on 𝐶𝑂2 concentration. Because

people breathe out 𝐶𝑂2, the higher the level, the more people are in the

space relative to the ventilation rate. With a 𝐶𝑂2 sensor DCV system, the

ventilation rate varies based on the number of people in the space [84].

For Siemens [82] the benefits offered by demand-controlled ventilation are

the following:

Automatic provision of optimum ventilation

An increased sense of well-being and higher productivity

Energy cost savings of 20 to 70% and, hence, less damage to the

environment

Good Internal Air Quality (IAQ), supported by documentary

evidence

The indoor air humidity level depends also on the level of humidity in the

outdoor air that is brought indoors by ventilation, human respiration, and

activities such as showering, cooking, and washing. For a good IAQ, the

relative humidity should fall in the range of 30%-70% [85]. For this reason

4 The effect of automatic control on building energy need/use

101

some ventilation system have also humidity control and humidity sensor in

addition to 𝐶𝑂2 sensors.

It is possible use passive methods, regulated by a specific automatic

control, to decrease the energy request from mechanical ventilation.

Among these methods there is night cooling. Night cooling refers to the

operation of natural ventilation at night in order to purge excess heat and

cool the building fabric. A building with sufficient thermal mass, which

can be exposed to night-time ventilation, can reduce peak daytime

temperatures by 2° to 3° using this strategy (http://www.passivent.com/).

During night or unoccupied period the windows could be open to obtain a

good air exchange, if the difference between external and internal

temperature it is not too strong. Usually the windows operation is activated

if the difference in below 10°C [86].

Table 4.5 gathers a summary situation gives by norm EN 15232 about

ventilation automatic control.

Table 4.5 Ventilation automatic control in buildings: summary table Norm EN 15232

Ventilation and air conditioning control

1.1 Air flow control at the room level

0 No automatic control

The systems runs constantly (e.g. manual controlled switch)

1 Time control

The system runs according to a given time schedule

2 Presence control

The systems runs dependent on the presence

3 Demand control

The system is controlled by sensors measuring the number of people or indoor air

parameters or adapted criteria. The used parameters shall be adapted to the kind of

activity in the space

1.2 Air flow or pressure control at the air handler level

0 No automation control

Continuously supplies of air flow for a maximum load of all rooms

1 On/off time control

Continuously supplies of air flow for a maximum load of all rooms during nominal

occupancy time

2 Multi-stage control

To reduce the auxiliary energy demand of the fan

3 Automatic flow or pressure control

With or without pressure reset, with or without demand evaluation: load depending

supplies of air flow for the demand of all connected rooms

1.3 Heat recovery exhaust air side icing protection control (if present)

0 Without defrost control

Therese is no specific action during cold period

1 With defrost control

4 The effect of automatic control on building energy need/use

102

During cold period a control loop enables to warranty that the air temperature leaving

the heat exchanger is not too low to avoid frosting

1.4 Heat recovery control prevention of overheating (if present)

0 Without overheating control

Therese is no specific action during hot or mild periods

1 With overheating control

During periods where the effect of the heat exchanger will no more be positive a control

loop between “stops” and “modulates” or bypass the heat exchanger

1.5 Free mechanical cooling

0 No automatic control

1 Night cooling

The amount of outdoor air is set to its maximum during the unoccupied period

provided:

- The room temperature is above the set point for comfort period

- The difference between the room temperature and the outdoor temperature is

above a given limit.

If free night cooling will be realized by automatically opening windows there is no air

flow control.

2 Free cooling

The amount of outdoor air and recirculation air are modulated during all periods of time

to minimize the amount of mechanical cooling. Calculation is performed on the basis of

temperatures

3 H,x-directed control

The amount of outdoor air and recirculation air are modulated during all periods of time

to minimize the amount of mechanical cooling. Calculation is performed on the basis of

temperatures and humidity (enthalpy)

1.6 Supply air temperature control

0 No automation control

1 Constant set point

A control loop enables to control the supply air temperature, the set point is constant

and can only be modified by a manual action

2 Variable set point with outdoor temperature compensation

A control loop enables to control the supply air temperature. The set point is a simple

function of the outdoor temperature (e.g. linear function)

3 Variable set point with load dependant compensation

A control loop enables to control the supply air temperature. The set point is defined as

a function of the loads in the room. This can normally only be achieved with an

integrated control system enabling to collect the temperatures or actuator position in the

different rooms

1.7 Humidity control

0 No automatic control

1 Dewpoint control

Supply air or room air humidity expresses the Dewpoint temperature and reheat of the

supply air

2 Direct humidity control

Supply air or room air humidity; a control loop enables the supply air or room air

humidity at a constant value

4 The effect of automatic control on building energy need/use

103

4.1.4 Blind System

Blinds are widely used in both commercial and residential buildings to

maintain occupants’ visual and comfort and privacy, as well as to reduce

energy use for heating cooling, and/or lighting [87]. Two are the main

motivation for blind control: solar protection to avoid overheating and

lowering the cooling load, and solar protection to avoid glare [87-89].

The blind control is very simple, a motorized actuator closes or opens the

blinds when the control conditions are satisfied.

The main control type are: solar type, shading is active if beam plus diffuse

solar radiation incident on the window exceeds the solar set-point, and

glare type, shading is on if the total daylight glare index at the one’s first

daylighting sensor from all of the exterior windows in the zone exceeds the

maximum glare index specified in the daylight zone.

So Young Koo et al. [87] study different blind control strategies, which

may be needed according to the energy requirements for heating or cooling

in buildings. In the non-cooling period when the air conditioner is off the

position of the lower end of a blind that maximizes daylight penetration is

necessary.

In the cooling period when the air conditioner is on, daylight might not be

useful for cooling energy savings for all sky conditions. Thus, to minimize

energy consumption, if the negative impact of daylight on cooling energy

consumption exceeds the positive impact of daylight on lighting energy

consumption a blind could be further lowered.

Table 4.6 gathers a summary situation gives by norm EN 15232 about

blind automation control

Table 4.6 Blind automatic control in buildings: summary table from Norm EN 15232

Blind Control

There are two different motivations for blind control: solar protection to avoid overheating and

to avoid glare

0 Manual operation:

Mostly used only for manual shadowing, energy saving depends only on the user

behaviour

1 Motorized operation with manual control:

Mostly used only for easiest manual (motor supported) shadowing, energy saving

depends only on the user behaviour

2 Motorized operation with automatic control:

Automatic controlled dimming to reduce cooling energy

3 Combined light/blind/HVAC control:

To optimize energy use for HVAC, blind and lighting for occupied and non-occupied

rooms

4 The effect of automatic control on building energy need/use

104

4.2 Energy simulation of a case study To better understand the effects of automatic controls on buildings, an

energy simulation of a case study was processed. The building is a

kindergarten located in Milan. The software used for the simulation is

EnergyPlus with DesignBuilder interface.

In order to reduce the high heating loads, the kindergarten is going to be

retrofitted, making improvements on the building’s envelope, Figure 4.8,

shows the building’s plant, instead Table 4.7 shows 𝑈𝑣𝑎𝑙𝑢𝑒 of the building

envelope and the g-value of windows before and after the retrofitting

works. The simulation was conducted both on the two models of the

existing building and retrofitted building, to compare the difference of

applying an automatic control on a building with bad performance (the

existing one) and on a building with high performance (the retrofitted one).

Figure 4.6(8) Kindergarten’s plant Table 4.7 Data of the building envelope before and after the retrofitting works

Existing building Retrofitted building

𝑈𝑣𝑎𝑙𝑢𝑒 walls 1,00 𝑊

𝑚2𝐾 0,09

𝑊

𝑚2𝐾

𝑈𝑣𝑎𝑙𝑢𝑒 roof 0,92 𝑊

𝑚2𝐾 0,09

𝑊

𝑚2𝐾

𝑈𝑣𝑎𝑙𝑢𝑒 floor 0,83𝑊

𝑚2𝐾 1,30

𝑊

𝑚2𝐾

𝑈𝑣𝑎𝑙𝑢𝑒 external window 5,78 𝑊

𝑚2𝐾 0,78

𝑊

𝑚2𝐾

g-value external window 0,82 0,47

𝑈𝑣𝑎𝑙𝑢𝑒 frame external window 5,88 𝑊

𝑚2𝐾 -*

𝑈𝑣𝑎𝑙𝑢𝑒 internal window 2,18𝑊

𝑚2𝐾 3,1

𝑊

𝑚2𝐾

g-value internal window 0,67 0,7

4 The effect of automatic control on building energy need/use

105

𝑈𝑣𝑎𝑙𝑢𝑒 frame internal window 3,63 𝑊

𝑚2𝐾 3,63

𝑊

𝑚2𝐾

𝑈𝑣𝑎𝑙𝑢𝑒 skylights 2,18 𝑊

𝑚2𝐾 0,78

𝑊

𝑚2𝐾

g-value skylights 0,68 0,47

𝑈𝑣𝑎𝑙𝑢𝑒 frame skylights 3,66 𝑊

𝑚2𝐾 -*

*In the retrofitted building the windows’ frame is included in the

performance of the opaque envelope.

The net floor area for the existing building is of 855 𝑚2, instead the

retrofitted building’s net floor area is of 873,5 𝑚2; this is due to the

incorporation of the two patios in the building area in the renovation

project.

The kindergarten is open from 7:30 AM to 6:00 PM from Monday to

Friday; for the simulation we considered also the school holiday calendar

and, in addition the kindergarten is closed during the month of August.

The weather file used is Milano-Linate file equipped from the

DesignBuilder servers.

The software gives to us, as final data the energy use of lighting and

equipment of the building and the building’s energy need for heating and

cooling. The energy need can be transformed in energy use by appropriate

factor:

47% as coefficient to pass from energy need to energy use for

heating in the existing building. It is the average seasonal efficiency

of the heating system and it was calculated by an energy audit

performed by A2A (network manager). It is a very low value

below the threshold of 81,4% established by Italian law. In Table

4.8 are reported all the efficiencies of the heating system.

~100% as coefficient to pass from energy need to energy use for

heating in the retrofitted building. We assumed a global efficiency

of the heating system of the retrofitted building equal to 1, because

the distribution system is well insulated and quite short and the

efficiency of the district heating heat exchanger is quite high

according to data provided by A2A.

The cooling system does not exist in the existing building. In this

work, we do not want to model in detail the cooling system but we

are interested to see if the automatic control on lighting and on

solar screen has some effect respect to an ideal condition (for

losses) and typical for the generation. So we decided to consider an

ideal system without any losses (factor equal to 1) and it was also

hypothesized the use of a heat pump (Quadra Inverter MHPR 85

VPS [90]) with an E.E.R of 3,71.

4 The effect of automatic control on building energy need/use

106

To compare the different values of energy between them, it is necessary to

transform them into primary energy.

To switch from electricity energy use to primary energy use it was used a

factor gave by GSE equal to 2,18

The primary energy conversion factor for the district heating network is

0,8, this data is also provided by A2A.

For the primary energy conversion factor for natural gas, after an accurate

literature analysis, we decided not to use the value provided by standard

UNI EN 15603, because it was considered too high and because this value

was calculated in 1996. A recent study by Ecofys in different European

country, [91] a study of the Department of Energy and Climate Change

[92] and at least a study conducted by EPA (Environmental Protection

Agency) [93], shows that the energy conversion factor for natural gas is

approximately 1,05. For simplicity in calculation we adopted a value of 1.

Table 4.8 summarizes all the initial data of the simulation.

Table 4.8 Initial data for the simulation

Net Floor Area (existing building) 855 𝑚2

Net Floor Area (retrofitted building) 873,5 𝑚2

General Schedule of the day-care centre (kindergarten) From 7:30 AM to

6:00 PM

Weather file source IGDG (Italian climatic data collection Gianni

de Giorgio)

Milano/Linate

Heating regulation efficiency 𝜂𝑟𝑒𝑔(existing building) 82,1%

Heating distribution efficiency 𝜂𝑑𝑖𝑠 (existing building) 88%

Heating emission efficiency 𝜂𝑒𝑚𝑖 (existing building) 92%

Heating general efficiency 𝜂𝑔𝑒𝑛(existing building) 85,4%

Heating Average seasonal efficiency �̅� (existing building) 47%

𝜂ℎ𝑒𝑎𝑡𝑖𝑛𝑔 𝑠𝑦𝑠𝑡𝑒𝑚 (retrofitted building) (information from A2A) ~100%

Ideal cooling system efficiencies 1

E.E.R (both for existing and retrofitted building) 3,71

National primary energy conversion factor for electricity 𝑓𝑝_𝑒𝑙

(both for existing and retrofitted building) (Source GSE)

2,18

Primary energy conversion factor for district heating 𝑓𝑝_𝑑𝑖𝑠ℎ𝑒𝑎𝑡

(retrofitted building) (information from A2A)

0,8

Primary energy conversion factor natural gas 𝑓𝑝_𝑛𝑎𝑡𝑔𝑎𝑠 (existing

building) obtained from literature analysis

1

For both the existing and the retrofitted building, following the standard

UNI EN ISO 15251, the set point temperature for heating period was set at

20°C, the intermediate temperature of the class I, for the highest thermal

comfort (see Figure 4.9). Instead, the set point temperature for cooling was

set at 26°C to obtain greater energy savings.

4 The effect of automatic control on building energy need/use

107

Figure 4.9 Recommended design values of the indoor temperature for design of

buildings and HVAC systems for Kindergarten (Norm UNI EN ISO 15215)

The calculation of cooling degree days (CDD) and heating degree days

(HDD) was made using the following equations:

𝐻𝐷𝐷 = ∑(𝑇𝑏 − �̅�𝑑𝑎𝑦)+

365

𝑖=1

𝐶𝐷𝐷 = ∑(�̅�𝑑𝑎𝑦 − 𝑇𝑏)+

365

𝑖=1

Where 𝑇𝑏 is the set point temperature respectively for heating and cooling

and �̅�𝑑𝑎𝑦 the average daily temperature. HDD are calculated only for the

heating period and CDD are calculated only for the cooling period.

For both the existing and the retrofitted building an illuminance target of

300 Lux was assigned accordingly to norm UNI EN 12464-1, excluding

for corridors, to which a illuminance of 100 Lux was assigned, always

accordingly to norm UNI EN 12464-1 (see Figure 4.10)

Figure 4.10 Illuminance for day-care and corridors from norm UNI EN 12464-1

The lighting system in both the existing and the retrofitted building follows

the schedule of the different rooms, if no occupants are in no illumination

is applied. For example the kitchen works from 10:30 AM to 11:30 PM

4 The effect of automatic control on building energy need/use

108

and also the classrooms have different schedules. Table 4.9 shows the

kindergarten’s schedules.

Table 4.9 Rooms’ different schedule

Room Schedule

1 Riposo Divezzi From 1:00 PM to 3:00 PM

2 Didattica Divezzi From 7:30 AM to 11:30 AM – from 3:00 PM to 6:00 PM

3 Lavanderia From 10:00 AM to 12:00 AM

4 Riposo Lattanti From 1:00 PM to 3:00 PM

5 Cucina From 10:30 AM to 11:30 PM

6 Mensa From 10:00 AM to 11:00 AM – from 1:00 PM to 2:00 PM –

from 4:00 PM to 5:00 PM

7 Didattica Lattanti From 7:30 AM to 11:30 AM – from 3:00 PM to 6:00 PM

8 Didattica Divezzini From 1:00 PM to 3:00 PM

9 Riposo Divezzini From 11:30 AM to 1:00 PM

10 Ordinate Divezzini From 1:00 PM to 3:00 PM

All other rooms From 7:30 AM to 6:00 PM

Figure 4.11 shows rooms’ position in the kindergarten’s plant. The

number of the room coincides with the numbering used in Table 4.9.

Figure 4.11 Room’s position (the numbers are the same used in Table 4.9)

In the two buildings mechanical ventilation is not consider. Instead are

considered infiltrations and they are: for the retrofitted building 0,3 𝑣𝑜𝑙

ℎ𝑜𝑢𝑟

and for the existing building 0,5 𝑣𝑜𝑙

ℎ𝑜𝑢𝑟, the infiltrations are applied for 24

hours and 365 days.

4 The effect of automatic control on building energy need/use

109

The windows operation follows the occupation schedule for each room as

shown before in Table 4.9, moreover another control condition based on

different internal and external temperature is applied:

If 𝑇𝑜𝑢𝑡 − 𝑇𝑖𝑛 < 5°C the windows are open to 100%

If 𝑇𝑜𝑢𝑡 − 𝑇𝑖𝑛 > 15°C the windows are closed to 100%

5 < 𝑇𝑜𝑢𝑡 − 𝑇𝑖𝑛 > 15°C there is a linear decay.

Figure 4.12 shows the windows operation condition.

This kind of windows operation could simulate an automatic control on

windows or the behaviour of a person particularly careful of the internal

conditions. However no literature review study had be conducted on this

topic.

Table 4.10 summarizes all the boundary condition of the simulations.

Table 4.10 boundary condition of the simulation

𝑇𝑠𝑒𝑡_𝑝𝑜𝑖𝑛𝑡 for cooling (both existing and

retrofitted building)

26 °C

𝑇𝑠𝑒𝑡_𝑝𝑜𝑖𝑛𝑡 for heating (both existing and

retrofitted building)

20°C

Heating season October to April

Cooling season May to September (August building

empty)

Cooling degree days 3,24 ° CDD

Heating degree days 2794° HDD

Lighting request to all rooms except the

corridors (both for existing and retrofitted

building)

300 Lux

Lighting request to corridors (both existing

and retrofitting building)

100 Lux

Illumination logic Following the occupation schedule for

each room (if no occupants is in no

% W

ind

ow

s o

pen

ing

𝑇𝑜𝑢𝑡 − 𝑇𝑖𝑛

5

15

Figure 4.12 Control used for windows operation

100%

4 The effect of automatic control on building energy need/use

110

illumination is applied)

Infiltration (existing building) 0,5 𝑣𝑜𝑙

ℎ𝑜𝑢𝑟 for 24h 365 days

Infiltration (retrofitted building) 0,3 𝑣𝑜𝑙

ℎ𝑜𝑢𝑟 for 24h 365 days

Mechanical ventilation (both for existing

and retrofitted building)

No mechanical ventilation is considered

Windows operation logic level 1

Following the occupation schedule for

each room (if no occupant is in no

illumination is applied)

Windows operation logic level 2 𝑇𝑜𝑢𝑡 − 𝑇𝑖𝑛 < 5°C the windows are open to

100%

𝑇𝑜𝑢𝑡 − 𝑇𝑖𝑛 > 15°C the windows are closed

to 100%

5 < 𝑇𝑜𝑢𝑡 − 𝑇𝑖𝑛 > 15°C there is a linear

decay.

4.2.1 Effect of automatic control of lighting in a case study

In both of the two buildings the existing and the retrofitted a daylight

control for lighting was simulated. The control is a dimming type, as

described in Chapter 4.1.2, the overhead lighting system dims continuously

and linearly from maximum electric power, maximum light output to

minimum electric power, minimum light output as the daylight illuminance

increases (see Figure 4.13).

Figure 4.13 Logic of the dimming control for lighting

The minimum input power fraction is the power fraction reached just

before the lights switch off and it was set at 10% of the nominal power.

4 The effect of automatic control on building energy need/use

111

The effect of the daylight control was studied in the existing building for

two type of lamps. The first are T12 fluorescent lamps, which are the

lamps now installed in the building, and the second are Light Emitting

Diode (LED). To simulate the different behaviour of the two type of lamps

we used the template available from DesignBuilder. The characteristics of

the bulbs are reported in Table 4.11 and Table 4.12. Instead in the

retrofitted building we simulate only the LED.

Table 4.11 T12 Fluorescent characteristics

T12 (37 mm diameter) Fluorescent, halophosphate

Nominal power 5 𝒘

𝒎𝟐∗𝟏𝟎𝟎 𝑳𝒖𝒙

Table 4.12 LED characteristics

LED Nominal Power 3,3

𝒘

𝒎𝟐∗𝟏𝟎𝟎 𝑳𝒖𝒙

In addition to daylight control, the behaviour of solar screen control and

how they influence daylight has been studied. The solar screens are

simulated on the existing and on the retrofitted one. For the type of solar

screen we chose blind with medium reflectivity slats, and we used the

appropriate template available on DesignBuilder. The control used for

solar screen is solar type: shading is active if beam and diffuse solar

radiation incident on the window exceeds the solar set point. The solar set

point of the solar screen was set to 200 𝑊

𝑚2, 300 𝑊

𝑚2 and 400 𝑊

𝑚2 to find the

optimal solution from the energy point of view. The solar screen operates

only during the cooling periods and it is disabled during the heating period.

In all the following reflections, we consider only the energy point of view.

No evaluation of visual comfort and glare were performed in our analysis.

Recapping there are 12 cases divided in this manner:

1) Existing Building with T12

2) Existing building with T12 + daylight control

3) Existing building with T12 + solar screen

a) Solar screen control set to 200𝑊

𝑚2

b) Solar screen control set to 300 𝑊

𝑚2

c) Solar screen control set to 400 𝑊

𝑚2

4) Existing building with T12 + daylight control + solar screen

a) Solar screen control set to 200𝑊

𝑚2

b) Solar screen control set to 300 𝑊

𝑚2

4 The effect of automatic control on building energy need/use

112

c) Solar screen control set to 400 𝑊

𝑚2

5) Existing building with LED

6) Existing building with LED + daylight control

7) Existing building with LED + solar screen

a) Solar screen control set to 200𝑊

𝑚2

b) Solar screen control set to 300 𝑊

𝑚2

c) Solar screen control set to 400 𝑊

𝑚2

8) Existing building with LED + daylight control + solar screen

a) Solar screen control set to 200𝑊

𝑚2

b) Solar screen control set to 300 𝑊

𝑚2

c) Solar screen control set to 400 𝑊

𝑚2

9) Retrofitted building with LED

10) Retrofitted building with LED + daylight control

11) Retrofitted building with LED + solar screen

a) Solar screen control set to 200𝑊

𝑚2

b) Solar screen control set to 300 𝑊

𝑚2

c) Solar screen control set to 400 𝑊

𝑚2

12) Retrofitted building with LED + daylight control + solar screen

a) Solar screen control set to 200𝑊

𝑚2

b) Solar screen control set to 300 𝑊

𝑚2

c) Solar screen control set to 400 𝑊

𝑚2

The simulation number 1 is on the existing building with T12 and this

simulation could be considered the reference for the calculation of energy

savings. Table 4.13 shows the monthly consumption in 𝑘𝑊ℎ

𝑚2 of the

building. As described before, we have energy use for lighting and

equipment and energy need for space heating and cooling. For

completeness the table of all the case are reported in Annex I.

4 The effect of automatic control on building energy need/use

113

Table 4.13 Monthly consumption for case 1 (energy use and need)

Equipment (El)

[𝒌𝑾𝒉

𝒎𝟐 ]

Lighting (El)

[𝒌𝑾𝒉

𝒎𝟐 ]

Heating (Gas)

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Cooling (El)

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 1,63 39,53 0,00

Feb 0,51 1,42 28,53 0,00

Mar 0,54 1,49 14,72 0,00

Apr 0,57 1,56 6,35 0,00

May 0,59 1,63 0,00 0,37

Jun 0,51 1,42 0,00 2,48

July 0,59 1,63 0,00 7,63

Aug 0,00 0,00 0,00 0,00

Sept 0,54 1,49 0,00 1,30

Oct 0,59 1,63 6,32 0,00

Nov 0,54 1,49 19,06 0,00

Dec 0,54 1,49 33,35 0,00

Total 6,12 16,90 147,86 11,77

Figure 4.14 reports a typical example of the monthly energy breakdown of

the kindergarten. Because all the other graphs have similar trends, they are

reported only in Annex I.

Figure 4.14 Case 1 monthly energy breakdown (energy use and need)

Using the lighting control (case 2) a saving of 84,6% is obtained for

lighting use, however the reduced energy generated by the lamps also

influenced the heating and cooling load. The heating load increases by

4,5%, instead the cooling load decreases by the 19,5 % (see Table 4.14).

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/(𝒎

^𝟐)]

Month

Cooling

Heating

Lighting

Equipment

4 The effect of automatic control on building energy need/use

114

Table 4.14 Total consumption of case 1 and case 2 and percentage variation

Equipment (El)

[𝒌𝑾𝒉

𝒎𝟐 ]

Lighting (El)

[𝒌𝑾𝒉

𝒎𝟐 ]

Heating (Gas)

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Cooling (El)

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Total case 1 6,12 16,90 147,86 11,77

Total case 2 6,12 2,60 153,53 9,48

Percentage

variation

- -84,6% +4,5% -19,5%

More saving can be obtained switching from T12 lamps to more

performing LED (case 5). Table 4.15 shows the saving in when replacing

T12 lamps without consider daylight control.

The lighting energy use saving is 34%. The same value can be calculated

using the nominal power of the two type of illumination, respectively 5 w

m2∗100 Lux and 3,3

w

m2∗100 Lux . The variation is not so significant, this is

because the existing lamps have already an average performance.

Table 4.15 Total consumption of case 1 and case 5 and percentage variation

Equipment (El)

[𝒌𝑾𝒉

𝒎𝟐 ]

Lighting (El)

[𝒌𝑾𝒉

𝒎𝟐 ]

Heating (Gas)

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Cooling (El)

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Total case 1 6,12 16,90 147,86 11,77

Total case 5 6.12 11,6 150,82 10,96

Percentage

variation

- -34% +2% -7%

Table 4.16 shows the saving in case 6 LED + daylight control applied.

Table 4.16 Total consumption of case 6 and percentage variation between the

previous case

Equipment (El)

[𝒌𝑾𝒉

𝒎𝟐 ]

Lighting (El)

[𝒌𝑾𝒉

𝒎𝟐 ]

Heating (Gas)

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Cooling (El)

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Total case 6 6,12 1,73 155,29 9,49

Saving

compared

case 1

- -89,77% +5% -19,41%

Saving

compared

case 5

-84,5% +3% -13,39%

The lighting energy saving of the case 6 compared to case one is of

approximately 90%. However compared to case 2 (the T12 lamps with

lighting control), the saving is only few percent point greater, 90%

compared to 84,6 %. This leads to say that the advantage of replacing the

T12 lamps with LED is grater in case we do not consider the daylight

control. Without the daylight control, replacing T12 with LED saves 5,3

4 The effect of automatic control on building energy need/use

115

kWh

m2, instead with the daylight control we save only 0,9

kWh

m2. Therefore the

installation of LED is less effective compared to the installation of daylight

system.

It is also useful to observe the changes in the consumption of primary

energy among the different cases. Table 4.17 reports the annual primary

energy consumption for case 1-2-5 and 6.

Table 4.17 Annual primary energy consumption

Equipment

[𝒌𝑾𝒉

𝒎𝟐 ]

Lighting

[𝒌𝑾𝒉

𝒎𝟐 ]

Heating

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Cooling

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Total Cons.*

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Case 1 13,34 36,85 314,59 6,92 371,70

Case 2 13,34 5,68 328,79 5,57 353,38

(-4,93%)

Case 5 13,34 24,32 320,09 6,44 365

(-1,8%)

Case 6 13,34 3.77 330,40 5,58 353,08

(-5%)

*The percentage variation of the Total consumption is referred with

respect to case 1

From the results in Table 4.17 we can observe that the percentage variation

in total primary energy consumption is very low and that case 2 and case 6

are also equal in term of primary energy.

The reason why a variation of 90% in lighting leads to a saving on total

primary energy of only 5% is visible from Figure 4.15. As we can see

from the annual energy breakdown the heating load have a great weight on

the total consumption (it is approximately 85% of the total consumption),

while lighting is less than 10% of the total primary energy consumption.

The use of lighting control thus leads in this case to a small energy

percentage saving on the total primary consumption.

As we could see later in the retrofitted building, that have lower heating

consumption the benefits brought by the lighting control will higher.

4 The effect of automatic control on building energy need/use

116

Figure 4.15 Annual energy breakdown for case 1 (primary energy)

Finally, for greatly clarity, Figure 4.16 Shows the annual primary energy

comparison of the different considered case.

Now we study the effect of the solar screen’s control on loads of the

existing building both with T12 lamps and LED (case 3, case 4, case 7 and

case 8 ).

Figure 4.17 shows the difference between the energy need for cooling in

case 1 and the energy need for cooling using the solar screen for different

solar set point control (case 3a, 3b and 3c), remembering that when the

solar radiation in 𝑊

𝑚2 is higher than the set point control, and all the others

required conditions are met, then the window shading is activated.

As assumed, the delta cooling energy need increases with decreasing the

solar set point for which the control is activated. Also, the cases 7a, 7b and

7c, have the same tendency. Heating load do not change because the solar

screens are activated only during the cooling periods.

The total annual primary energy reduction using the solar screen is very

limited, in the best case (case 3a), is lower than 1% (0,80%). This is

because cooling load is approximately 1,8% of the total primary energy

consumption.

All the table with the monthly result of solar screen are reported always in

Annex 1.

0

50

100

150

200

250

300

350

Pri

mar

y En

erg

y [k

Wh

/(𝒎

^𝟐)]

Equipment

Lighting

Heating

Cooling

4 The effect of automatic control on building energy need/use

117

Figure 4.16 Annual Primary energy comparison between case 1 case 2 case 5 and

case 6

The situation is rather different when we consider solar screen and daylight

control (case 4). In fact the optimal situation for the cooling load it is also

the worse for lighting control, as we could see from Figure 4.18, where the

delta lighting primary energy and delta cooling primary energy between

case 1 and respectively case 4a, 4b and 4c are reported.

Is important to note that also in this case the cooling load decreases, this is

due to the fact that the decrease of solar heat gain acting on the building is

predominant respect the increase of the heat gain generated by the lamps.

340

345

350

355

360

365

370

375P

rim

ary

Ene

rgy

[kW

h/(𝒎

^𝟐)]

Primary energy comparison

LED+ Daylight Control (6) LED (5) T12 + Daylight Control (2) T12 (1)

4 The effect of automatic control on building energy need/use

118

Figure 4.17 Change in reduction cooling energy need, changing the solar set point

(case 3)

Figure 4.18 Change in delta lighting and cooling primary energy changing the solar

set point (case 4)

It is therefore necessary to find a trade-off between the reduction of

cooling energy and the increase of lighting energy use, minimizing the

total primary energy expenditure.

Figure 4.19 shows the variation of total annual primary energy of case 4

and case 8 with respect to the appropriate case without the solar screen,

0

1

2

3

4

5

6

100 150 200 250 300 350 400 450

red

uct

ion

co

olin

g e

ne

rgy

ne

ed

[

kWh

/(𝒎

^𝟐)]

Solar set point [W/(m^2)]

-1,5

-1

-0,5

0

0,5

1

1,5

2

2,5

3

200 300 400

[kW

h/(𝒎

^𝟐)]

Solar set point [W/(m^2)]

Delta lighting primaryenergy

Delta cooling primaryenergy

4 The effect of automatic control on building energy need/use

119

varying the solar set point. As we could see from the figure in case 4 the

solar set point at 200 𝑊

𝑚2 and at 300

𝑊

𝑚2 are equivalent instead the set point

at 400 𝑊

𝑚2, leads to the worst scenario.

In case 8 (existing building + daylight control and solar screen) instead the

best available solution is to set the control at 200 𝑊

𝑚2.

Also with the daylight control case the total annual primary energy

reduction generated by the control of the solar screen is very lower, under

1%. In the best case we obtain a reduction of 0,35% of the total primary

energy respect case 2.

We now analyse the retrofitted building. Table 4.18 reports the monthly

energy consumption (energy need and energy use) of the building in 𝑘𝑊ℎ

𝑚2 .

The heating energy need are lower than in case of the existing building:

14,36 𝑘𝑊ℎ

𝑚2 compared to 147,86 𝑘𝑊ℎ

𝑚2 , demonstrating the excellent

retrofitting work.

Figure 4.19 Reduction of the total primary energy consumption for case 4 and for

case 8 changing the solar set point.

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

2

0 100 200 300 400 500

Re

du

ctio

n p

rim

ary

en

erg

y [k

Wh

/(m

^2)]

Solar set point [W/(m^2)]

Case 4

Case 8

4 The effect of automatic control on building energy need/use

120

Table 4.18 Monthly consumption for case 9 (energy use and need)

Equipment (El)

[𝒌𝑾𝒉

𝒎𝟐 ]

Lighting (El)

[𝒌𝑾𝒉

𝒎𝟐 ]

Heating (Gas)

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Cooling (El)

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,57 1,12 5,21 0,00

Feb 0,50 0,98 3,04 0,00

Mar 0,52 1,03 0,47 0,00

Apr 0,55 1,08 0,01 0,00

May 0,57 1,12 0,00 0,5

Jun 0,50 0,98 0,00 1,57

July 0,57 1,12 0,00 3,53

Aug 0,00 0,00 0,00 0,00

Sept 0,52 1,03 0,00 0,96

Oct 0,57 1,12 0,00 0,00

Nov 0,52 1,03 1,13 0,00

Dec 0,5, 1,03 4,51 0,00

Total 5,19 11.64 14,36 6,56

Figure 4.20 shows the graph of the monthly energy breakdown of the

kindergarten.

Figure 4.20 Case 9 monthly energy breakdown (energy use and need)

As in the previously cases, the use of a daylight control leads to a lighting

energy use saving around 80%, and at the same time influences the heating

0

1

2

3

4

5

6

7

8

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/(

m^2

)]

Month

Cooling

Heating

Lighting

Equipment

4 The effect of automatic control on building energy need/use

121

and cooling load. The heating load arise by 17% instead the cooling load

decreases by 17% (See Table 4.19).

Table 4.19 Total consumption of case 9 and case 10 and percentage variation

Equipment

[𝒌𝑾𝒉

𝒎𝟐 ]

Lighting

[𝒌𝑾𝒉

𝒎𝟐 ]

Heating

(Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Cooling

(El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Total case 9 5,19 11,64 14,36 11,77

Total case 10 5,19 2,56 16,78 5,42

Percentage

variation

- -78,03% +16,8% -17,3%

The fundamental difference between the existing building and the

retrofitted building can be observed in the primary energy savings. In fact

in these case the use of lighting control leads to 34,6% of primary energy

savings as reported in Table 4.20.

Table 4.20 Total Primary energy consumption of case 9 and case 10

Equipment

[𝒌𝑾𝒉

𝒎𝟐 ]

Lighting

[𝒌𝑾𝒉

𝒎𝟐 ]

Heating

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Cooling

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Total Cons.

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Case 9 12,89 25,37 11,49 3,85 53,6

Case 10 12,89 5,57 13,43 3,19 35

(-34,6%)

In fact, as we can see from the total primary energy breakdown (Figure

4.21), in the retrofitted building, lighting energy use becomes the main

expenditure of the building. Instead heating, due to the retrofitting works

and the replacement of the standard boiler with district heating connection,

becomes the third expenditure voice in term of primary energy.

Therefore there are more energy advantages to install a lighting automatic

control on building with high performance with respect to a building with

low performance, in term of primary energy.

4 The effect of automatic control on building energy need/use

122

Figure 4.21 Annual energy breakdown for case 9 (primary energy)

The solar screen, without the daylight control have the same trend

observed for the existing building (see Figure 4.22). However, with the

daylight control the buildings reaches the best performance when the solar

set point is set at 400 𝑊

𝑚2, this is the opposite solution compared to the case

of the existing building, in fact when the control is set at 200 𝑊

𝑚2 the

primary energy savings is almost zero (see Figure 4.23).

These results demonstrate that there is not an absolute best solution for all

buildings, but that each building must be studied to find its optimum

energy solutions.

In this case the total annual primary energy reduction using the solar screen

control is: in the best case without daylight control (case 11a)

approximately 2%, instead with daylight control (case 12c) is

approximately 1,5%.

Therefore, the effects of the solar screen on the total annual primary energy

reduction are more visible in the retrofitted building. This is because the

cooling load has a greater weight on the total load respect the existing

building. So in this case the control on solar screen works better on a high

performance building.

0

5

10

15

20

25

30

Pri

mar

y e

ne

rgy

[kW

h/(

m^2

)]

Equipment

Lighting

Heating

Cooling

4 The effect of automatic control on building energy need/use

123

Figure 4.22 Change in delta cooling energy need, changing the solar set point (case

11)

Figure 4.23 Total primary energy consumption of case 12 changing the solar set

point.

In the end we compare the primary energy use of the existing building and

of the best case of the retrofitted building (case 12c), to observe what could

be the total primary energy savings at the end of the retrofitting works and

with the installation of daylighting control and solar screen with solar set

point control (see Table 4.21)

0,00

0,20

0,40

0,60

0,80

1,00

1,20

1,40

1,60

1,80

2,00

0 100 200 300 400 500

Re

du

ctio

n c

oo

ling

en

erg

y n

ee

d

[kW

h/(𝒎

^𝟐)]

Solar set point [W/(m^2)]

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0 100 200 300 400 500

Re

du

ctio

n p

rim

ary

en

erg

y [K

wh

/(m

^2)]

Solar set point [W/(m^2)]

4 The effect of automatic control on building energy need/use

124

Table 4.21 Comparison between case 1 and case 12c (primary energy use)

Equipment

[𝒌𝑾𝒉

𝒎𝟐 ]

Lighting

[𝒌𝑾𝒉

𝒎𝟐 ]

Heating

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Cooling

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Total Cons.

[ 𝒌𝑾𝒉

𝒎𝟐 ]

Case 1 13,34 36,85 314,59 6,92 371,70

Case 12c 12,89 5,78 13,43 2,46 34,56

(-90,7%)

At the end we have an expected primary energy reduction of 90%.

4.2.1.1 Cost esteem of the lighting and solar screen control system

In this paragraph we make a rough esteem of the capital cost of the lighting

and solar screen control. All the prices reported afterwards are taken from

Prezziario Regione Piemonte 2014 [94].

Table 4.22 gathers all the principal components necessary to create a

building automation system.

It is important to specify that the building is composed of 29 rooms and

possesses 47 windows that need the solar screen control.

Table 4.22 Main necessary components’ number and cost to create a building

automation system.

Equipment Number Price [€]

Power supply unit 1 236,29 €

Coupler 1 359,79 €

Central interface 1 220,31 €

BUS cables 400 m* 272,00 €

Control panel 1 1891,97 €

Repeater 29** 33343,62 €

Internet 1 895,58 €

BUS Interface 29** 2314,78 €

Switching system 29** 5101,39 €

Climate station 1 2258,45 €

Motor actuator 47*** 9381,2 €

Lamps actuator 29** 20048,8 €

Data acquisition system and

sensors

29** 4405,68 €

Total - 76324,47 €

*For the BUS cables we hypothesized that it will serve an amount

approximately equal to the building’s perimeter multiplied by two.

**There is one of these elements in each building’s rooms.

***One of these elements it is necessary for all buildings windows.

4 The effect of automatic control on building energy need/use

125

However, a market analysis of BACS, showed that the prices of the

Prezziario Regione Piemonte are slightly higher than the prices present on

market (approximately of 35%). So we chose 50000,00 € as a more reliable

price for our BACS.

To the necessary components for the building automation system, we must

add the expenditure for the replacement of the T12 with LED and the

replacement of the ceiling lights. The building has 138 lamps, divided as

described in Table 4.23. The prices of the LED are taken from the price

list provided by Philips [95], the prices of the ceiling lights are taken from

Prezziario Regione Lombardia 2011 [96] (see Table 4.24).

Table 4.23 LED’s number and prices

LED Number Price[€]*

58W 22 1181,18 €

36W 101 4174,33 €

18W 15 619,95 €

Total - 5975,46 €

*VAT excluded

Table 4.24 Type, Number and price for the buildings’ ceiling lights

Ceiling Lights Number Price [€]

1x58W 6 432,12 €

2x58W 8 707,20 €

1x36W 1 61,48 €

2x36W 50 3871,00 €

1X18W 15 710,10 €

Total - 5781,80 €

The total price for interventions is of 61757,26 € (VAT of the LED

excluded), to which installation costs are to be added. The installation costs

for Prezziario Regione Lombardia 2011 [96] are of 35,23 €/hour.

We want to know in the case of the existing building the pay-back time of

the BACS only, of the replacement of the lights and of the union of the

intervention, while in the case of the retrofitted building the pay-back time

of the installation of the BACS only.

The advantage of the installation of the BACS is the lower bill that the

kindergarten may pay, both for the lighting and for cooling. From the

analysis of current bills it was possible to establish an energy variable cost

of 0,24 €/kWh, the interest rate chosen is 3%, a standard value. The results

of the analysis for the existing building are reported in Table 4.25.

4 The effect of automatic control on building energy need/use

126

Table 4.25 Pay-back time for the existing building

Case Pay-back time

Replacing lights (existing building) 13 years

Only BACS (existing building) 24 years

BACS + Replacing lights (existing building)

Only BACS (retrofitted building) 30 years

>30 years

From the results we observe that:

Replacing the T12 with LED is the best economic option for our

building, even if it leads to save less energy.

To obtain a good pay-back time, a BACS that controls only lighting

and solar screen, is feasible only if an elevate solar heat gain or

high lighting expenditure are considered such as in many office

buildings.

Approximately for the same price of 50000,00 € we could realize a

better BACS, that controls also the equipment and the mechanical

ventilation using heat recovery to obtain a higher reduction of the

pay-back time and a better air quality in the rooms occupied by the

students.

The pay-back time for the retrofitted building is slightly higher than

the pay-back time of the existing building installing only the

BACS. This is due to the fact that the retrofitted building has

already lower electric consumption for lighting and cooling.

4.2.2 Calculation to create a ZEB on annual and monthly base

Starting from analysis described above, we tried to calculate how many

square meters of photovoltaic panels are necessary to transform the retrofit

kindergarten in a ZEB. We chose photovoltaic panels as renewable

technology, because other solution such as geothermal pumps and wind

turbines are not compatible with kindergarten’s location and architectural

aspect.

As we said in Chapter 2 there are two ways to calculate a ZEB; the first is

on annual base: the energy produced by the renewable plant in one year

must be equal to energy consumed by the building in the same period of

time. The second is on monthly base: the energy produced by the

renewable plant must be equal or plus to energy consumed each month. As

we see later the two approaches lead to very different results.

In addition to the two ways to calculate a ZEB we observed the difference

of changing the primary energy factor for the electricity produced by the

PV system. In the first case we use as primary energy factor 1, and in

second case we use 2,18, since there is no a specific regulations about these

argument; this choice leads to substantial different results. For the

4 The effect of automatic control on building energy need/use

127

simulation of the PV system we used PVsyst software. We used a

polycrystalline panels with medium performance (efficiency of 12,8 %). In

the case of annual zero energy balance we used the annual optimum

configuration: panels’ inclination equal to 35° and south exposition (see

Figure 4.24a). For the monthly zero energy balance we dimensioned the

PV systems in such a way that produce enough energy in the month with

worst conditions (December), so we use the winter optimum configuration:

panels’ inclination 56° and south exposition (see Figure 4.24b) We

compared the square meters of PV panels necessary to create a ZEB in case

9, case 10 and the best option of case 11 and 12 (case 11a and case 12c).

For price we decided to not consider the price of Prezziario Regione

Lombardia [75], which is equal to 6000 €/kW of the nominal power, but

rather to refer to the market price of 3000 €/kW of the installed nominal

power.

Table 4.26 gathers the result for annual simulation for case 9. With the

primary conversion factor equal to one the electric energy generated by the

PV system is equal to primary energy. This factor choice penalizes the

systems, in fact more square meters are necessary compared to the case

with primary conversion factor equal to 2,18 To obtain this result around

263 𝑚2 of PV polycrystalline panels are necessary.

Figure 4.24a Collector plane

orientation and optimisation for

annual yield

Figure 4.24b Collector plane

orientation and optimisation for

winter period

4 The effect of automatic control on building energy need/use

128

Table 4.26 Monthly data for case 9 and 𝐟𝐩=1

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟏)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 1922 1922 7050

Feb 2577 2577 5063

Mar 4036 4036 3361

Apr 5061 5061 3178

May 5356 5356 3577

Jun 5575 5575 3709

July 6083 6083 5176

Aug 5812 5812 0

Sept 4645 4645 3534

Oct 3357 3357 3316

Nov 2015 2015 3838

Dec 1661 1661 6259

Tot. 48100 48100 48061

Figure 4.25 shows the primary energy used by the building and the

primary energy generated by the PV system. As we could see if annual

primary energy use is equal to annual primary energy generated by the PV

system, this equation is not verified each month. In winter the building

consumes more energy than the one produced by the PV system, and vice

versa in summer.

Figure 4.25 Monthly comparison between primary energy used by the building and

primary energy generated by the PV (case 9 and ZEB on annual basis)

0

1000

2000

3000

4000

5000

6000

7000

8000

1 2 3 4 5 6 7 8 9 10 11 12

[Kw

h]

Month

Primary energy used Primary energy generated by PV

4 The effect of automatic control on building energy need/use

129

For case 9 with primary conversion factor equal to 2,18 only 120 𝑚2 of PV

panels are required. The monthly data are reported in Table 4.27. Since

there is not regulation in relation which primary conversion factor to use,

large imbalances could be created in ZEB regulation.

Table 4.27 Monthly data for case 9 and 𝐟𝐩=2,18

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟐, 𝟏𝟖)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 883 1925 7050

Feb 1184 2581 5063

Mar 1854 4042 3361

Apr 2325 5069 3178

May 2461 5365 3577

Jun 2562 5585 3709

July 2795 6093 5176

Aug 2670 5821 0

Sept 2134 4652 3534

Oct 1542 3362 3316

Nov 926 2019 3838

Dec 763 1663 6259

Tot 22100 48176 48061

Table 4.28 and Figure 4.26, show the case of a ZEB on a monthly base.

As we can see the energy produced by the PV must be equal every month

to the energy consumed by the building. The sizing of the PV system must

be done on the worst month in this case December. As we could see from

the table below the primary energy produced by the building in one year is

more than the energy consumed by the building: it is a prosumer building

(see Chapter 2). In this case the PV’s area is 870,5 𝑚2 , three times the

area of the ZEB based on annual basis. This is the main reason for which

this choice is the less used when ZEB has to be built.

Table 4.28 Monthly data for case 9 and 𝐟𝐩=1 (ZEB on monthly bases)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟏)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 7133 7133 7050

Feb 9108 9108 5063

Mar 13358 13358 3361

Apr 15699 15699 3178

May 15602 15602 3577

Jun 15720 15720 3709

July 17330 17330 5176

Aug 17459 17459 0

Sept 15079 15079 3534

4 The effect of automatic control on building energy need/use

130

Oct 11633 11633 3316

Nov 7371 7371 3838

Dec 6254 6260 6259

Tot 151746 151746 48061

The other case was calculated with the same method of these overlying. -

all the table of monthly data for all the cases are reported in Annex II.

Figure 4.26 Monthly comparison between primary energy used by the building and

primary energy generated by the PV (case 9 and ZEB on monthly basis)

Table 4.29 gathers all the final results of the calculations. As we can see

there is a greater difference of square meters of PV panels depending on

the different choices of primary energy factor and base of calculation.

If we consider the best case of total primary energy consumption (case

12c) we need between 78 m2 and 784 m2 of PV panels to create a ZEB, at

a cost that varies between 34800,00 € and 354000,00 €. It is obvious that

the second choice is unfeasible both for the PV panels installation

necessary space and for economic point of view.

In a building without daylight control and solar screen control (case 9) we

need, instead, between 121 m2 and 871 m2 of PV panels.

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h]

Month

Primary energu used Primary energy generated by PV

4 The effect of automatic control on building energy need/use

131

Table 4.29 Recapping of necessary PV panels to create a ZEB in different case

𝒎𝟐 of PV panels

[𝒎𝟐]

Nominal Power

[kW]

Cost

[€]

Case 9 𝑓𝑝=1 annual base 263 39,4 118200,00 €

Case 9 𝑓𝑝=2,18 annual base 121 18,1 54300,00 €

Case 9 𝑓𝑝=1 monthly base 870,5 131 456000,00 €

Case 9 𝑓𝑝=2,18 monthly base 400 60 180000,00 €

Case 10 𝑓𝑝=1 annual base 172 25,8 77400,00 €

Case 10 𝑓𝑝=2,18 annual base 79 11,8 35400,00 €

Case 10 𝑓𝑝=1 monthly base 784 118 354000,00 €

Case 10 𝑓𝑝=1 monthly base 360 54 162000,00 €

Case 11a 𝑓𝑝=1 annual base 258 38.7 116100,00 €

Case 11a 𝑓𝑝=2,18 annual base 118 17.7 53100,00 €

Case 11a 𝑓𝑝=1 monthly base 870,5 131 456000,00 €

Case 11a 𝑓𝑝=1 monthly base 400 60 180000,00 €

Case 12c 𝑓𝑝=1 annual base 169 25,4 76200,00 €

Case 12c 𝑓𝑝=2,18 annual base 78 11,6 34800,00 €

Case 12c 𝑓𝑝=1 monthly base 784 118 354000,00 €

Case 12c 𝑓𝑝=2,18 monthly base 360 54 162000,00 €

4 The effect of automatic control on building energy need/use

132

5 Conclusions

133

5 Conclusions

In this thesis we analysed how some automatic controls can help

decreasing the energy consumption of existing buildings, characterized by

low or high envelope performance and how they can help realizing a

smarted city from the energy point of view. Under this perspective, they

may be called smart buildings.

Although, a common and shared definition of smart building does not yet

exist, is possible to say that a smart building is not a building with a lot of

ICT inside, but a building where ICT contributes to an optimization and

reduction of the building’s energy use.

To better understand the effect of the automatic control on building

performance we analysed a case study of a kindergarten located in Milan.

In particular we studied the lighting and blind control on two models: the

existing building (as it is now) and the retrofitted building (after a deep

renovation of building envelope and systems).

In the existing building we observed that the installation of a lighting

dimming control, may lead to better performance than only replacing the

fluorescent T12 lights, present in the building, with LED.

We saw that, the installation of automatic control leads always to better

energy performance; but the effects of the control on the total primary

energy requirements are higher on a building with high performance and

low energy consumption than a building with low performance and high

energy consumption. In fact, in the retrofitted kindergarten there is a

potential 35% saving on the total primary energy use of the building, using

the lighting and blind automatic control, while it is reduce to only 5%

saving of total primary energy in the case of the existing kindergarten.

This is due to the fact that, once heating and cooling energy needs are

substantially reduced by a deep energy retrofit, other energy uses such as

lighting become predominant in the energy breakdown of buildings, this a

proper control of them becomes fundamental for the energy management.

In a system with lighting control and solar blind control, it is necessary to

find a trade-off between the reduction of the cooling energy use generated

by the solar screen and the rise of the lighting energy use, caused by

closure of the blinds and the consequent reduction of the natural daylight.

In our case study there is not a condition that works both for the existing

building and the retrofitted building, instead two different solutions have

been found, again as function of the different building envelope.

The pay-back time of the Building Automation and Control System

(BACS) was evaluated around 25 years in the case of the existing building

and 30 years in the retrofitted building (because the retrofitted building

include already high performance LED lamps, while the existing lighting

5 Conclusions

134

system is made of fluorescent lamps). This shows that a BACS that

controls only lighting and solar blind, is economically sustainable only in a

building with high solar heat gain and/or high lighting requirements, such

as many existing office buildings. We observed that with very slight

increase of the capital cost, the simulates BACS of the kindergarten could

also control a mechanical/hybrid ventilation system and equipment, such as

washing machines and dryers, and this could determine a reduction of the

payback time.

By 31 December 2020 (31 December 2018 for buildings occupied and

owned by public authorities), all new buildings in EU member States

should be nearly zero-energy buildings. EU member States shall draw up

national plans for increasing the number of nearly zero-energy buildings,

reflecting national, regional or local conditions.

Many definitions of zero-energy building are available in the literature,

discussing on how to establish the energy balance (monthly, yearly, etc.)

on what kind of energy or other indicator use in the balance, and on what

boundaries to consider (the building walls, the construction site, the district

etc.).

Utilizing the retrofitted kindergarten as a case study, it is possible to notice

that there is a profound difference between designing a ZEB on annual

base (the energy consumed by the building must be equal to the energy

produced by the building during a year) or on monthly base (the energy

produced by the building must be equal to the energy consumed by the

building every month). For example, in terms of square meters of PV

panels necessary to produce enough energy, we can pass from 78 𝑚2 to

360 𝑚2 and this has a substantial effect also on the economic feasibility of

the investment.

We also noticed that in case of a zero energy balance calculated on

monthly base we have a prosumer building: a building that produces more

energy than the energy consumed during a year.

In the literature there is also a strong debate on the choice of the most

appropriate primary energy factor to be applied to the electrical energy

produced on site by the PV system. If results obtained using a usual unitary

factor are contrasted against results obtained using the national primary

energy conversion factor the required PV panel surface pass from 784 𝑚2

to 360 𝑚2.

These choices about nearly and zero energy building calculation are still

under debate; further studies are nevertheless required to find a univocal

calculation approach, if more effective solutions toward real energy smart

buildings and a real energy smart city want to be achieved.

ANNEX I

135

ANNEX I

In this annex all the table of kindergarten’s energy simulation are included.

The figures of the main monthly energy breakdown are also comprised.

Table AI.1 Monthly consumption for case 1 (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 1,63 39,53 0,00

Feb 0,51 1,42 28,53 0,00

Mar 0,54 1,49 14,72 0,00

Apr 0,57 1,56 6,35 0,00

May 0,59 1,63 0,00 0,37

Jun 0,51 1,42 0,00 2,48

July 0,59 1,63 0,00 7,63

Aug 0,00 0,00 0,00 0,00

Sept 0,54 1,49 0,00 1,30

Oct 0,59 1,63 6,32 0,00

Nov 0,54 1,49 19,06 0,00

Dec 0,54 1,49 33,35 0,00

Total 6,12 16,90 147,86 11,77

Figure AI. 1 Case 1 monthly energy breakdown (energy use and need)

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/(

m^2

)]

Month

Cooling

Heating

Lighting

Equipment

ANNEX I

136

Table AI.2 Monthly consumption for case 2 (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 0,55 40,55 0,00

Feb 0,51 0,32 29,55 0,00

Mar 0,54 0,15 15,90 0,00

Apr 0,57 0,08 7,13 0,00

May 0,59 0,03 0,00 0,27

Jun 0,51 0,02 0,00 1,89

July 0,59 0,00 0,00 6,38

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,09 0,00 0,93

Oct 0,59 0,23 7,27 0,00

Nov 0,54 0,47 19,99 0,00

Dec 0,54 0,65 34,13 0,00

Total 6,12 2,60 154,53 9,48

Figure AI. 2 Case 2 monthly energy breakdown (energy use and need)

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/(

m^2

)]

Month

Cooling

Heating

Lighting

Equipment

ANNEX I

137

Table AI.3 Monthly consumption for case 3a (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 1,63 39,53 0,00

Feb 0,51 1,42 28,53 0,00

Mar 0,54 1,49 14,72 0,00

Apr 0,57 1,56 6,35 0,00

May 0,59 1,63 0,00 0,16

Jun 0,51 1,42 0,00 1,24

July 0,59 1,63 0,00 4,76

Aug 0,00 0,00 0,00 0,00

Sept 0,54 1,49 0,00 0,57

Oct 0,59 1,63 6,32 0,00

Nov 0,54 1,49 19,06 0,00

Dec 0,54 1,49 33,35 0,00

Total 6,12 16,90 147,86 6,73

Table AI.4 Monthly consumption for case 3b (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 1,63 39,53 0,00

Feb 0,51 1,42 28,53 0,00

Mar 0,54 1,49 14,72 0,00

Apr 0,57 1,56 6,35 0,00

May 0,59 1,63 0,00 0,18

Jun 0,51 1,42 0,00 1,60

July 0,59 1,63 0,00 5,72

Aug 0,00 0,00 0,00 0,00

Sept 0,54 1,49 0,00 0,70

Oct 0,59 1,63 6,32 0,00

Nov 0,54 1,49 19,06 0,00

Dec 0,54 1,49 33,35 0,00

Total 6,12 16,90 147,86 8,21

ANNEX I

138

Table AI.5 Monthly consumption for case 3c (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 1,63 39,53 0,00

Feb 0,51 1,42 28,53 0,00

Mar 0,54 1,49 14,72 0,00

Apr 0,57 1,56 6,35 0,00

May 0,59 1,63 0,00 0,21

Jun 0,51 1,42 0,00 1,97

July 0,59 1,63 0,00 6,59

Aug 0,00 0,00 0,00 0,00

Sept 0,54 1,49 0,00 0,92

Oct 0,59 1,63 6,32 0,00

Nov 0,54 1,49 19,06 0,00

Dec 0,54 1,49 33,35 0,00

Total 6,12 16,90 147,86 9,69

Table AI.6 Monthly consumption for case 4a (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 0,55 39,53 0,00

Feb 0,51 0,32 28,53 0,00

Mar 0,54 0,15 14,72 0,00

Apr 0,57 0,08 6,35 0,00

May 0,59 0,17 0,00 0,15

Jun 0,51 0,15 0,00 0,96

July 0,59 0,19 0,00 3,98

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,17 0,00 0,39

Oct 0,59 0,23 6,32 0,00

Nov 0,54 0,47 19,06 0,00

Dec 0,54 0,65 33,35 0,00

Total 6,12 3,12 147,86 5,48

ANNEX I

139

Table AI.7 Monthly consumption for case 4b (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 0,55 39,53 0,00

Feb 0,51 0,32 28,53 0,00

Mar 0,54 0,15 14,72 0,00

Apr 0,57 0,08 6,35 0,00

May 0,59 0,08 0,00 0,16

Jun 0,51 0,07 0,00 1,22

July 0,59 0,09 0,00 4,75

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,14 0,00 0,48

Oct 0,59 0,23 6,32 0,00

Nov 0,54 0,47 19,06 0,00

Dec 0,54 0,65 33,35 0,00

Total 6,12 2,82 147,86 6,60

Table AI.8 Monthly consumption for case 4c (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 0,55 39,53 0,00

Feb 0,51 0,32 28,53 0,00

Mar 0,54 0,15 14,72 0,00

Apr 0,57 0,08 6,35 0,00

May 0,59 0,05 0,00 0,18

Jun 0,51 0,04 0,00 1,46

July 0,59 0,06 0,00 5,41

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,10 0,00 0,63

Oct 0,59 0,23 6,32 0,00

Nov 0,54 0,47 19,06 0,00

Dec 0,54 0,65 33,35 0,00

Total 6,12 2,70 147,86 7,68

ANNEX I

140

Table AI.9 Monthly consumption for case 5 (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 1,08 40,04 0,00

Feb 0,51 0,94 28,97 0,00

Mar 0,54 0,98 15,16 0,00

Apr 0,57 1,03 6,62 0,00

May 0,59 1,08 0,00 0,33

Jun 0,51 0,94 0,00 2,27

July 0,59 1,08 0,00 7,20

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,98 0,00 1,17

Oct 0,59 1,08 6,69 0,00

Nov 0,54 0,98 19,52 0,00

Dec 0,54 0,98 33,82 0,00

Total 6,12 11,16 150,82 10,96

Figure AI.3 Case 5 monthly energy breakdown (energy use and need)

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/(

m^2

)]

Month

Cooling

Heating

Lighting

Equipment

ANNEX I

141

Table AI.10 Monthly consumption for case 6 (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 0,36 40,72 0,00

Feb 0,51 0,21 29,65 0,00

Mar 0,54 0,10 15,95 0,00

Apr 0,57 0,05 7,16 0,00

May 0,59 0,02 0,00 0,28

Jun 0,51 0,02 0,00 1,90

July 0,59 0,02 0,00 6.38

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,06 0,00 0,94

Oct 0,59 0,15 7,34 0,00

Nov 0,54 0,31 20,14 0,00

Dec 0,54 0,43 34,34 0,00

Total 6,12 1,73 155,29 9,49

Figure AI.4 Case 6 monthly energy breakdown (energy use and need)

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/(

m^2

)]

Month

Cooling

Heating

Lighting

Equipment

ANNEX I

142

Table AI.11 Monthly consumption for case 7a (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 1,08 40,04 0,00

Feb 0,51 0,94 28,97 0,00

Mar 0,54 0,98 15,16 0,00

Apr 0,57 1,03 6,62 0,00

May 0,59 1,08 0,00 0,15

Jun 0,51 0,94 0,00 1,10

July 0,59 1,08 0,00 4,39

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,98 0,00 0,48

Oct 0,59 1,08 6,69 0,00

Nov 0,54 0,98 19,52 0,00

Dec 0,54 0,98 33,82 0,00

Total 6,12 11,16 150,82 6,11

Table AI.12 Monthly consumption for case 7b (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 1,08 40,04 0,00

Feb 0,51 0,94 28,97 0,00

Mar 0,54 0,98 15,16 0,00

Apr 0,57 1,03 6,62 0,00

May 0,59 1,08 0,00 0,17

Jun 0,51 0,94 0,00 1,44

July 0,59 1,08 0,00 5,32

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,98 0,00 0,60

Oct 0,59 1,08 6,69 0,00

Nov 0,54 0,98 19,52 0,00

Dec 0,54 0,98 33,82 0,00

Total 6,12 11,16 150,82 7,53

ANNEX I

143

Table AI.13 Monthly consumption for case 7c (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 1,08 40,04 0,00

Feb 0,51 0,94 28,97 0,00

Mar 0,54 0,98 15,16 0,00

Apr 0,57 1,03 6,62 0,00

May 0,59 1,08 0,00 0,19

Jun 0,51 0,94 0,00 1,75

July 0,59 1,08 0,00 6,12

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,98 0,00 0,80

Oct 0,59 1,08 6,69 0,00

Nov 0,54 0,98 19,52 0,00

Dec 0,54 0,98 33,82 0,00

Total 6,12 11,16 150,82 8,87

Table AI.14 Monthly consumption for case 8a (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 0,36 40,72 0,00

Feb 0,51 0,21 29,65 0,00

Mar 0,54 0,10 15,95 0,00

Apr 0,57 0,05 7,16 0,00

May 0,59 0,11 0,00 0,14

Jun 0,51 0,09 0,00 0,94

July 0,59 0,12 0,00 3,94

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,11 0,00 0,39

Oct 0,59 0,15 7,34 0,00

Nov 0,54 0,31 20,14 0,00

Dec 0,54 0,43 34,34 0,00

Total 6,12 2,04 155,29 5,41

ANNEX I

144

Table AI.15 Monthly consumption for case 8b (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 0,36 40,72 0,00

Feb 0,51 0,21 29,65 0,00

Mar 0,54 0,10 15,95 0,00

Apr 0,57 0,05 7,16 0,00

May 0,59 0,05 0,00 0,16

Jun 0,51 0,04 0,00 1,21

July 0,59 0,06 0,00 4,73

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,09 0,00 0,47

Oct 0,59 0,15 7,34 0,00

Nov 0,54 0,31 20,14 0,00

Dec 0,54 0,43 34,34 0,00

Total 6,12 1,85 155,29 6,57

Table AI.16 Monthly consumption for case 8c (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,59 0,36 40,72 0,00

Feb 0,51 0,21 29,65 0,00

Mar 0,54 0,10 15,95 0,00

Apr 0,57 0,05 7,16 0,00

May 0,59 0,03 0,00 0,18

Jun 0,51 0,03 0,00 1,46

July 0,59 0,04 0,00 5,40

Aug 0,00 0,00 0,00 0,00

Sept 0,54 0,07 0,00 0,62

Oct 0,59 0,15 7,34 0,00

Nov 0,54 0,31 20,14 0,00

Dec 0,54 0,43 34,34 0,00

Total 6,12 1,78 155,29 7,67

ANNEX I

145

Table AI.17 Monthly consumption for case 9 (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,57 1,12 5,21 0,00

Feb 0,50 0,98 3,04 0,00

Mar 0,52 1,03 0,47 0,00

Apr 0,55 1,08 0,01 0,00

May 0,57 1,12 0,00 0,50

Jun 0,50 0,98 0,00 1,57

July 0,57 1,12 0,00 3,53

Aug 0,00 0,00 0,00 0,00

Sept 0,52 1,03 0,00 0,96

Oct 0,57 1,12 0,00 0,00

Nov 0,52 1,03 1,13 0,00

Dec 0,52 1,03 4,51 0,00

Total 5,91 11,64 14,36 6,56

Figure AI.5 Case 9 monthly energy breakdown (energy use and need)

0

1

2

3

4

5

6

7

8

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/(

m^2

)]

Month

Cooling

Heating

Lighting

Equipment

ANNEX I

146

Table AI.18 Monthly consumption for case 10 (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,57 0,48 5,79 0,00

Feb 0,50 0,31 3,61 0,00

Mar 0,52 0,18 0.75 0,00

Apr 0,55 0,11 0,05 0,00

May 0,57 0,05 0,00 0,37

Jun 0,50 0,04 0,00 2,48

July 0,57 0,04 0,00 7,63

Aug 0,00 0,00 0,00 0,00

Sept 0,52 0,12 0,00 1,30

Oct 0,57 0,25 0,02 0,00

Nov 0,52 0,42 1,60 0,00

Dec 0,52 0,54 4,96 0,00

Total 5,91 2,56 16.78 5,42

Figure AI.6 Case 10 monthly energy breakdown (energy use and need)

0

1

2

3

4

5

6

7

8

Gen Feb Mar Apr Mag Giu Lug Ago Sett Ott Nov Dic

[kW

h/(

m^2

)]

Month

Cooling

Heating

Lighting

Equipment

ANNEX I

147

Table AI.19 Monthly consumption for case 11a (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,57 1,12 5,21 0,00

Feb 0,50 0,98 3,04 0,00

Mar 0,52 1,03 0,47 0,00

Apr 0,55 1,08 0,01 0,00

May 0,57 1,12 0,00 0,39

Jun 0,50 0,98 0,00 1,15

July 0,57 1,12 0,00 2,63

Aug 0,00 0,00 0,00 0,00

Sept 0,52 1,03 0,00 0,70

Oct 0,57 1,12 0,00 0,00

Nov 0,52 1,03 1,13 0,00

Dec 0,52 1,03 4,51 0,00

Total 5,91 11,64 14,36 4,87

Table AI.20 Monthly consumption for case 11b (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,57 1,12 5,21 0,00

Feb 0,50 0,98 3,04 0,00

Mar 0,52 1,03 0,47 0,00

Apr 0,55 1,08 0,01 0,00

May 0,57 1,12 0,00 0,41

Jun 0,50 0,98 0,00 1,27

July 0,57 1,12 0,00 2,91

Aug 0,00 0,00 0,00 0,00

Sept 0,52 1,03 0,00 0,74

Oct 0,57 1,12 0,00 0,00

Nov 0,52 1,03 1,13 0,00

Dec 0,52 1,03 4,51 0,00

Total 5,91 11,64 14,36 5,32

ANNEX I

148

Table AI.21 Monthly consumption for case 11c (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,57 1,12 5,21 0,00

Feb 0,50 0,98 3,04 0,00

Mar 0,52 1,03 0,47 0,00

Apr 0,55 1,08 0,01 0,00

May 0,57 1,12 0,00 0,44

Jun 0,50 0,98 0,00 1,39

July 0,57 1,12 0,00 3,16

Aug 0,00 0,00 0,00 0,00

Sept 0,52 1,03 0,00 0,82

Oct 0,57 1,12 0,00 0,00

Nov 0,52 1,03 1,13 0,00

Dec 0,52 1,03 4,51 0,00

Total 5,91 11,64 14,36 5,82

Table AI.22 Monthly consumption for case 12a (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,57 0,48 5,79 0,00

Feb 0,50 0,31 3,61 0,00

Mar 0,52 0,18 0,75 0,00

Apr 0,55 0,11 0,05 0,00

May 0,57 0,17 0,00 0,37

Jun 0,50 0,15 0,00 2,48

July 0,57 0,18 0,00 7,63

Aug 0,00 0,00 0,00 0,00

Sept 0,52 0,20 0,00 1,30

Oct 0,57 0,25 0,02 0,00

Nov 0,52 0,42 1,60 0,00

Dec 0,52 0,54 4,96 0,00

Total 5,91 3,01 16.78 11,77

ANNEX I

149

Table AI.23 Monthly consumption for case 12b (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,57 0,48 5,79 0,00

Feb 0,50 0,31 3,61 0,00

Mar 0,52 0,18 0,75 0,00

Apr 0,55 0,11 0,05 0,00

May 0,57 0,10 0,00 0,33

Jun 0,50 0,09 0,00 0,91

July 0,57 0,10 0,00 2,11

Aug 0,00 0,00 0,00 0,00

Sept 0,52 0,17 0,00 0,52

Oct 0,57 0,25 0,02 0,00

Nov 0,52 0,42 1,60 0,00

Dec 0,52 0,54 4,96 0,00

Total 5,91 2,76 16.78 3,87

Table AI.24 Monthly consumption for case 12c (energy use and need)

Equipment [𝒌𝑾𝒉

𝒎𝟐 ] Lighting [𝒌𝑾𝒉

𝒎𝟐 ] Heating (Gas)[ 𝒌𝑾𝒉

𝒎𝟐 ] Cooling (El)[ 𝒌𝑾𝒉

𝒎𝟐 ]

Jen 0,57 0,48 5,79 0,00

Feb 0,50 0,31 3,61 0,00

Mar 0,52 0,18 0,75 0,00

Apr 0,55 0,11 0,05 0,00

May 0,57 0,08 0,00 0,34

Jun 0,50 0,06 0,00 0,98

July 0,57 0,07 0,00 2,29

Aug 0,00 0,00 0,00 0,00

Sept 0,52 0,14 0,00 0,56

Oct 0,57 0,25 0,02 0,00

Nov 0,52 0,42 1,60 0,00

Dec 0,52 0,54 4,96 0,00

Total 5,91 2,65 16.78 4,18

ANNEX I

150

ANNEX II

151

ANNEX II

In this annex are reported all the case of energy production from PV

panels.

Table AII.1 Monthly data for case 9 and 𝐟𝐩=1 (annual based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟏)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 1922 1922 7050

Feb 2577 2577 5063

Mar 4036 4036 3361

Apr 5061 5061 3178

May 5356 5356 3577

Jun 5575 5575 3709

July 6083 6083 5176

Aug 5812 5812 0

Sept 4645 4645 3534

Oct 3357 3357 3316

Nov 2015 2015 3838

Dec 1661 1661 6259

Tot. 48100 48100 48061

Table AII.2 Monthly data for case 9 and 𝐟𝐩=2,18 (annual based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟐, 𝟏𝟖)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 883 1925 7050

Feb 1184 2581 5063

Mar 1854 4042 3361

Apr 2325 5069 3178

May 2461 5365 3577

Jun 2562 5585 3709

July 2795 6093 5176

Aug 2670 5821 0

Sept 2134 4652 3534

Oct 1542 3362 3316

Nov 926 2019 3838

Dec 763 1663 6259

Tot. 22099 48176 48061

ANNEX II

152

Table AII.3 Monthly data for case 9 and 𝐟𝐩=1 (monthly based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟏)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 7133 7133 7050

Feb 9108 9108 5063

Mar 13358 13358 3361

Apr 15699 15699 3178

May 15602 15602 3577

Jun 15720 15720 3709

July 17330 17330 5176

Aug 17459 17459 0

Sept 15079 15079 3534

Oct 11633 11633 3316

Nov 7371 7371 3838

Dec 6254 6254 6259

Tot. 151746 151746 48061

Table AII.4 Monthly data for case 9 and 𝐟𝐩=2,18 (monthly based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟐, 𝟏𝟖)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 3278 7146 7050

Feb 4185 9123 5063

Mar 6138 13381 3361

Apr 7214 15727 3178

May 7169 15628 3577

Jun 7223 15746 3709

July 7963 17359 5176

Aug 8002 17488 0

Sept 6929 15105 3534

Oct 5346 11654 3316

Nov 3387 7384 3838

Dec 2874 6265 6259

Tot. 69728 152007 48061

ANNEX II

153

Table AII.5 Monthly data for case 10 and 𝐟𝐩=1 (annual based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟏)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 1257 1257 6216

Feb 1685 1685 4164

Mar 2639 2639 1910

Apr 3309 3309 1333

May 3502 3502 1444

Jun 3645 3645 1738

July 3977 3977 2738

Aug 3800 3800 0

Sept 3037 3037 1659

Oct 2195 2195 1627

Nov 1318 1318 2997

Dec 1086 1086 5630

Tot. 31450 31450 31455

Table AII.6 Monthly data for case 10 and 𝐟𝐩=2,18 (annual based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟐, 𝟏𝟖)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 577 1258 6216

Feb 773 1685 4164

Mar 1211 2640 1910

Apr 1518 3309 1333

May 1607 3503 1444

Jun 1673 3647 1738

July 1825 3979 2738

Aug 1744 3802 0

Sept 1393 3037 1659

Oct 1007 2195 1627

Nov 605 1319 2997

Dec 498 1086 5630

Tot. 14431 31460 31455

ANNEX II

154

Table AII.7 Monthly data for case 10 and 𝐟𝐩=1 (monthly based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟏)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 6424 6424 6216

Feb 8203 8203 4164

Mar 12031 12031 1910

Apr 14139 14139 1333

May 14051 14051 1444

Jun 14158 14158 1738

July 15608 15608 2738

Aug 15724 15724 0

Sept 13581 13581 1659

Oct 10477 10477 1627

Nov 6639 6639 2997

Dec 5632 5632 5630

Tot. 136667 136667 31455

Table AII.8 Monthly data for case 10 and 𝐟𝐩=2,18 (monthly based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟐, 𝟏𝟖)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 2950 6431 6216

Feb 3767 8212 4164

Mar 5524 12042 1910

Apr 6492 14153 1333

May 6452 14065 1444

Jun 6501 14172 1738

July 7167 15624 2738

Aug 7220 15740 0

Sept 6236 13594 1659

Oct 4811 10488 1627

Nov 3048 6645 2997

Dec 2586 5637 5630

Tot. 62754 1368044 31455

ANNEX II

155

Table AII.9 Monthly data for case 11a and 𝐟𝐩=1 (annual based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟏)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 1887 1887 7050

Feb 2529 2529 5063

Mar 3961 3961 3361

Apr 4966 4966 3178

May 5255 5255 3521

Jun 5471 5471 3490

July 5969 5969 4699

Aug 5703 5703 0

Sept 4558 4558 3398

Oct 3294 3294 3316

Nov 1978 1978 3838

Dec 1630 1630 6259

Tot. 47201 47201 47174

Table AII.10 Monthly data for case 11a and 𝐟𝐩=2,18 (annual based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟐, 𝟏𝟖)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 865 1886 7050

Feb 1159 2527 5063

Mar 1816 3959 3361

Apr 2277 4964 3178

May 2409 5252 3521

Jun 2508 5467 3490

July 2737 5967 4699

Aug 2615 5701 0

Sept 2090 4556 3398

Oct 1510 3292 3316

Nov 907 1977 3838

Dec 747 1628 6259

Tot. 21640 47175 47174

ANNEX II

156

Table AII.11 Monthly data for case 11a and 𝐟𝐩=1 (monthly based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟏)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 7141 7141 7050

Feb 9118 9118 5063

Mar 13374 13374 3361

Apr 15717 15717 3178

May 15620 15620 3521

Jun 15738 15738 3490

July 17350 17350 4699

Aug 17479 17479 0

Sept 15096 15096 3398

Oct 11647 11647 3316

Nov 7379 7379 3838

Dec 6261 6261 6259

Tot. 151920 151920 47174

Table AII.12 Monthly data for case 11a and 𝐟𝐩=2,18 (monthly based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟐, 𝟏𝟖)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 3278 7146 7050

Feb 4185 9123 5063

Mar 6138 13381 3361

Apr 7214 15727 3178

May 7169 15628 3521

Jun 7223 15746 3490

July 7963 17359 4699

Aug 8022 17488 0

Sept 6929 15105 3398

Oct 5346 11654 3316

Nov 3387 7384 3838

Dec 2874 6265 6259

Tot. 69728 152007 47174

ANNEX II

157

Table AII.13 Monthly data for case 12c and 𝐟𝐩=1 (annual based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟏)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 1239 1239 6216

Feb 1661 1661 4164

Mar 2601 2601 1910

Apr 3262 3262 1333

May 3452 3452 1448

Jun 3593 3593 1611

July 3920 3920 2467

Aug 3746 3746 0

Sept 2994 2994 1583

Oct 2163 2163 1627

Nov 1299 1299 2997

Dec 1071 1071 5630

Tot. 31000 31000 30986

Table AII.14 Monthly data for case 12c and 𝐟𝐩=2,18 (annual based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟐, 𝟏𝟖)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 568 1238 6216

Feb 761 1659 4164

Mar 1192 2599 1910

Apr 1495 3259 1333

May 1582 3449 1448

Jun 1647 3590 1611

July 1797 3917 2467

Aug 1717 3743 0

Sept 1372 2991 1583

Oct 992 2163 1627

Nov 595 1297 2997

Dec 491 1070 5630

Tot. 48100 31000 30986

ANNEX II

158

Table AII.15 Monthly data for case 12c and 𝐟𝐩=1 (monthly based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟏)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 6424 6424 6216

Feb 8203 8203 4164

Mar 12031 12031 1910

Apr 14139 14139 1333

May 14051 14051 1448

Jun 14158 14158 1611

July 15608 15608 2467

Aug 15724 15724 0

Sept 13581 13581 1583

Oct 10477 10477 1627

Nov 6639 6639 2997

Dec 5632 5632 5630

Tot. 136667 136667 30986

Table AII.16 Monthly data for case 12c and 𝐟𝐩=2,18 (monthly based ZEB)

PV’s electric energy

[kWh]

PV’s primary energy

(𝒇𝒑 = 𝟐, 𝟏𝟖)

[kWh]

Primary energy

consumed by building

[kWh]

Jen 2950 6431 6216

Feb 3767 8212 4164

Mar 5524 12042 1910

Apr 6492 14153 1333

May 6452 14065 1448

Jun 6501 14172 1611

July 7167 15624 2467

Aug 7220 15740 0

Sept 6236 13594 1583

Oct 4811 10488 1627

Nov 3048 6645 2997

Dec 2586 5637 5630

Tot. 62754 1368044 30986

Bibliography

159

Bibliography

[1] Andrea Caragliu, Chiara Del Bo & Peter Nijkamp, “Smart Cities in

Europe”, Journal of Urban Technology (2011), 18:2, 65-82.

[2] Eduardo de Oliveira Fernandes et al., “Smart Cities Initiative: How to

Foster a Quick Transition Towards Local Sustainable Energy System”,

European University Institute (January 2011).

[3] Hafedh Chourabi, Taewoo Nam, Shawn Walker, J. Ramon Gil-Garcia,

Sehl Mellouli, Karine Nahon, Theresa A. Pardo, “Understanding Smart

Cities: An integrative Framework”, 45th

Hawaii International Conference

on System Sciences, (2012).

[4] Dough Washburn, Usman Sindhu, “Helping CIOs Understand Smart

City Initiatives”, Forrester (11 February 2010).

[5] Peter D. Lund, Jani Mikkola, J. Ypya, “Smart energy system design for

large clean power schemes in urban areas”. Journal of Cleaner Production,

Elsiever (9 February 2014).

[6] Susanne Dirks, Mary Keeling, “A vision of smarter cities”, IBM

(2009).

[7] Donato Toppeda, “The Smart City vision: How Innovation and ICT can

build smart, “liveable”, sustainable cities, THINK! REPORT 005/2010.

[8] Taewoo Nam & Theresa A. Pardo, “Conceptualizing Smart City with

Dimensions of Technology, People, and Institutions, The Proceedings of

the 12th

Annual International Conference on Digital Government

Research, (June 2011).

[9] Patrizia Lombardi, Silvia Giordano, Hend Farouh & Wael Yousef,

“Modelling the smart city performance”, Innovation: The European

Journal of Social Science Research (2012) 25:2, 137-149, Routledge.

[10] “Hitachi’s Vision for Smart Cities –Seeking the Optimal Balance

Among People, Places, Prosperity”, and the Planet, Hitachi (2013).

[11] Jung Hoon Lee, Marguerite Gong Hancock, Mei-Chih Hu, “Towards

an effective framework for building smart cities: Lessons from Seoul and

San Francisco”, Technological Forecasting & Social Change (2013),

Elsiever.

[12] Anna Kramers, Mattias Hojer, Nina Lovehagen, Josefin Wangel,

“Smart sustainable cities – Exploring ICT solutions for reduced energy use

in city”, Environmental Modelling & Software 56 (2014) 52-62, Elsiever.

[13] Catriona Manville, Gavin Cochrane & All., “Mapping Smart Cities in

the EU”, Policy Department A European Union (2014).

[14] Paolo Neirotti, Alberto De Marco, Anna Corinna Cagliano, Giulio

Mangano, Francesco Scorrano, “Current trends in Smart Cities initiatives:

Some stylised facts”, Cities (2014) 25-36, Elsiever.

Bibliography

160

[15] Stephen Mayer, Michael Narodoslawsky, “Optimal Renewable

Energy Systems for Smart Cities”, 24TH

European Symposium on

Computer Aided Process Engineering, Elsiever (2014).

[16] Rudolf Gliffinger, Christian Ferther, Hans Kramar et al., “Smart Cities

Ranking of European medium sized cities, Centre of Regional Science,

Vienna UT, October 2007.

[17] Margarita Angelidou, “Smart city policies: A spatial approach”, Cities

41 (2014) S3-S11, Elsiever.

[18] Roberto De Lieto Vollaro, Luca Evangelisti, Emiliano Carnielo,

Gabriele Battista, Paola Gori, Claudia Guattari, Aldo Fanchiotti, “An

Integrated Approach For an Historical Buildings Energy Analysis in a

Smart Cities Perspective”, 68th

Conference of the Italian Thermal

Machines Engineering Association, Elsiever (2013).

[19] B. Morvaj, L. Lugaric, S. Krajcar, “Demonstrating Smart Buildings

and Smart Grid features in a Smart Energy City”, IEEE.

[20] Maria V. Moreno, Miguel A. Zamora and Antonio F. Skarmeta,

“User-centric smart buildings for energy sustainable smart cities”, Trans.

Emerging Tel. Tech., 25:41-55 (2014).

[21] Rangan Banerjee et al. “Global Energy Assessment Toward a

Sustainable Future”, International Institute for Applied System Analysis

(2012).

[22] Pervez Hameed Shaikh, Nursyarizal Bin Mohd Nor, Parumal

Nallagownden, Irraivan Elamvazuthi, Taib Ibrahim, “A review on

optimized control systems for building energy and comfort management of

smart sustainable buildings”, Renewable and Sustainable Energy Reviews

34 (2014) 409-429, Elsiever.

[23] Natalija Lepkova, Domantas Zubka, Rasmus Lund Jensen “Global

Sustainable Communities Handbook” Chapter 10 “Financial Investements

for Zero Energy Houses: The Case of Near-Zero Energy Buildings” (2014)

Elsiever.

[24] Edwin Rodriguex-Ubinas et al. “Passive design strategies and

performance of Net Energy Plus Houses”, Energy and Buildings 83 (2014)

10-22, Elsiever.

[25] Wolfgang Feist Jurgen Schnieders, Viktor Dorer, Anne Hasse “Re-

inventing air heating: Convenient and comfortable within the frame of the

Passive House concept”, Energy and Buildings 37 (2005) 1186-1203,

Elsiever.

[26] Angeliki Kylili, Paris A. Fokaides, “European smart cities: The role of

zero energy buildings”, Sustainable Cities and Society 15 (2015) 86-95,

Elsiever.

[27] Edwin Rodriguez-Ubinas, Sergio Rodriguez, Karsten Voss, Marija S.

Todorovic, “Energy efficiency evaluation of zero energy houses”, Energy

and Buildings 83 (2014) 23-35, Elsiever.

Bibliography

161

[28] Giuliano Dall’O, Elisia Bruni, Luca Sarto, “An Italian pilot project for

zero energy buildings: Towards a quality-driven approach”, Renewable

Energy 50 (2013) 840-846, Elsiever.

[29] Stephen Berry, David Whaley, Wasim Saman and Kathryn Davidson,

“Reachin to net zero energy: The recipe to create zero energy homes in

warm temperate climates”, Energy Procedia 62 (2014) 112-122, Elsiever.

[30] A.J. Marszal, P. Heiselberg, J.S. Bourrelle, E. Musall, K. Voss,

I.Sartori, A. Napolitano “Zero Energy Building – A review of Definitions

and calculation methodologies”, Energy and Buildings 43 (2011) 971-979,

Elsiever.

[31] Alessandro Gallo, Bélen Téllez Molina, Milan Prodanovic, Josè

Gonzàlez Aguilar, Manuel Romero, “Analysis of net Zero-Energy Building

in Spain. Integratin of PV, solar domestic hot water and air-conditioning

systems”, Energy Procedia 48 (2014) 826-836, Elsiever.

[32] Alessandra Scognamiglio, Francois Garde, Harald N. Rotsvik, “How

Net Zero Energy Buildings and cities might look like? New challenges for

passive design and renewables design.”, Energy Procedia 61 (2014) 1163-

1166, Elsiever.

[33] Fabian Ochs, Georgios Dermentzis, Wolfgang Feist, “Minimization of

the residueal energy demand of multi-storey Passive Houses – energetic

and economic analysis of solar thermal and PV in combination with a heat

pump”, Energy Procedia 48 (2014) 1124-1133.

[34] Patxi Hernandez, Paul Kenny, “From net energy to zero energy

buildings: Defining life cycle zero energy buildings (LC-ZEB), Energy and

Buildings 42 (2010) 815-821, Elsiever.

[35] A.H Buckman, M. Mayfield Stephen, B. M. Beck, “What is a Smart

Building?”, Smart and Sustainable Built Environment, Vol. 3 Iss 2 (2014)

92-109 .

[36] AmirHosein GhaffarianHoseini, Nur Dalilah Dahlan, Umberto

Berardi, Ali GhaffarianHoseini, Nastaran Makaremi, “The Essence of

future smart houses: From embedding ICT to adapting to sustainability

principles”, Renewable and Sustainable Energy Reviews 24 (2012) 593-

607, Elsiever.

[37] Derek Clements-Croome, “Editorial”, Intelligent Buildings

Internetional (2012) 4:1, 1-3.

[38] Anna Kramers, Orjan Svane, “ICT applications for energy efficiency

in buildings”, KTH Centre for Sustainable Communications (2011).

[39] Siemens, “Improving Performance with Integrated Smart Buildings”,

www.usa.siemens.com.

[40] Zhu Wang, Lingfeng Wanng, Anastasios I. Dounis, Rui Yang, “Multi-

agent control system with information fusion based comfort model for

smart buildings”, Applied Energy 99 (2912) 247-254.

Bibliography

162

[41] The Climate Group, “Smart 2020: Enabling low carbon economy in

the information age, Creative Commons Attribution (2008).

[42] Ettore Cinarelli, Marherita Converso, “Smart building: il

miglioramento dell’efficienza energetica in edifici civili e del terziario

mediante la gestione, il monitoraggio e la supervision degli impianti”,

Congresso Nazionale AICA (2011).

[43] Massimiliano Manfren, Paola Caputo, Gaia Costa, “Paradigm Shift in

urban energy systems through distributed generation: Methods and

models”, Applied Energy 88 (2011) 1032-1048, Elsiever.

[44] Fang Wang, Jian Yang, Zhimin Wu, Xi Chen, Jianzhong Yu,

“Distributed energy system improving security for city energy supply”,

IEEE.

[45] U.S Energy Information Administration (EIA), “Modelling distributed

generation in the building sector”, Independent Statistics & Analysis

(2013).

[46] C.F. Calvillo, A. Sànchez, J. Villar, “Distributed Energy Generation in

Smart Cities”, International Conference on Renewable Energy Research

and Applications, IEEE (October 2013).

[47] Metthew B. Nissen, “High performance development as distributed

generation”, IEEE (2009).

[48] Jialin Wang, “Building Integrated Wind Energy”, Thesis submitted to

the University of Manchester for the degree of Doctor of Philosophy in the

Faculty of Engineering and Physical Science (2013).

[49] Pierluigi Mancarella, “Distributed Multi Generation Options to

Increase Environmental Efficiency In Smart Cities”, IEEE (2012).

[50] Kari Alanne, Arto Saari, “ Sustainable small-scale CHP technologies

for buildings: the basis for multi-perspective decision-making”, Renewable

and Sustainable Energy Reviews 8 (2014) 401-431, Elsiever.

[51] Salvador Ruiz-Romero, Antonio Colmenar-Santos, Francisco Mur-

Perez, Africa Lopez-Rey, “Integration of distributed generation in the

power distribution network: The need for smart grid control systems,

communication and equipment for a smart city- Use case”, Renewable and

Sustainable Energy Reviews 38 223-234, Elsiever (2014).

[52] Umberto Bertelè, Vittorio Chiesa, Maurizio Delfanti, “Smart Grid

Report: Le prospettive di sviluppo delle Energy Community in Italia, MIP

(2014).

[53] Antonello Gaviano, Karl Weber, Christian Dirmeier, “Challenges of

Integration of PV and Wind Energy Facilities from a Smart Grid Point of

View”, PV Asia Pacific Conference, Elsiever (2011).

[54] Andrea Immendoerfer, Markus Winkelmann, Volker Stelzer, “Energy

Solutions for Smart Cities and Communities”, European Union (2014).

[55] Thomas Lutzkendorf, Ellen Platt, Michael Kleber, Russell McKenna,

Erik Merkel, Kilian Seitz, Julian Stengel, “Evaluation of (Smart) Solutions

Bibliography

163

– Guidebook for Assessment Part II – Final Assessment Report”, European

Communities 2013.

[56] Gunther H. Oettinger, “CONCERTO: A cities’ guide to a sustainable

build environment”, European Communities (2010).

[57] Michael Kleber, Russell McKenna, “Energy Solutions for Smart

Cities and Communities: Lesson learnt from the 58 pilot cities of the

CONCERTO initiative” European Union (2014).

[58] Francesco Anzioso et al., “POLYCITY Energy Network in

Sustainable City”, Kramer (2012).

[59] Russell McKenna, Kilian Seitz, Micheal Kleber, “CONCERTO

Premium: Work package 1/3 Indicator Guide”, European Union (2012).

[60] Tobias Erhart, Aneta Stzalka, Ben Hassine Ilyes, Eric Duminil,

“Energy networks in sustainable city energy report”, European Union

(2011).

[61] “POLYCITY Project: Energetic and Urban Regeneration of the

Arquata District in the city of Torino”.

[62] Tobias Erhart, Aneta Stzalka, Ben Hassine Ilyes, Eric Duminil,

“Energy networks in sustainable city environmental report”, European

Union (2011).

[63] Jon Höller, Vlasios Tsiatsis, Catherine Mulligan, Stamatis

Karnouskos, Stefan Avensand, David Boyle “From Machine-to-Machine

to the Internet of Things” Chapter 13 “ Commercial Building Automation”,

Elsiever (2014).

[64] Robert Wolsey et al. “Lighting Answers: Controlling Lighting with

Building Automation Systems”, Rensselaer Polytechnic Institute (1997).

[65] Cisco Validated Design “Building Automation System over IP

(BAS/IP) Design and Implementation Guide”, Cisco (2008).

[66] Wolfgang Kastner, Georg Neugschwandtner, Stefan Soucek, H.

Micheal Newman, “Communication Systems for Building Automation and

Control”, Proceedings of the IEEE, Vol. 93, No.6, (June 2005).

[67] Scuola Edile Bresciana “La domotica e l’efficienza energetica negli

edifici”, (Marzo 2013).

[68] Siemens “Energy efficiency in building automation: Application guide

for heating and cooling supply” www.siemens.com/energyefficiency

(2013).

[69] Z. Liao, A.L. Dexter, “The potential for energy saving in heating

systems through improving boiler controls”, Energy and Buildings 36

(2004) 261-271, Elsiever.

[70] Rui Yang, Lingfenf Wang, “Optimal Control Strategy for HVAC

System in Building Energy Management”, IEEE (2012).

[71] Ryan Naughton, Muhammad Awais Abbas and J. Mikael Eklund,

“Comparison of apartment building heating control systems”, IEEE (2011).

Bibliography

164

[72] Mario Vašak, Anita Martinčević, “Optimal Control of a Family House

Heating System”, IEEE (2013).

[73] Honglian Thybo, Lars F.S. Larsen anf Claus Thybo, “Control of a

water-based floor heating system”, 16th

IEEE International Conference on

Control Application, IEEE (October 2007)

[74] L. Peeters, K. Van der Veken, H. Hens, L. Helsen, W. D’haeseleer,

“Control of heating systems in residential buildings: Current practice”,

Energy and Buildings 40 (2008) 1446-1455, Elsiever.

[75] Mohammad Asif ul Haq, Mohammad Yusri Hassan, Hayati Abdullah,

Hasimah Abdul Rahman, Md Pauzi Abdullah, Faridah Hussin, Dalila Mat

Said, “A review on lighting control technologies in commercial buildings,

their performance and affecting factors”, Renewable and Sustainable

Energy Reviews 33 (2014) 268-279, Elsiever

[76] C. Aghemo, J. Virgone, G.V. Fracastoro, A. Pellegrino, L. Blaso, J.

Savoyat, Kevyn Johannes, “Manangement and monitoring of public

buildings through ICT based systems: Control rules for energy saving with

lighting and HVAC services”, Frontiers of Architectural Research (2013)

2, 147-161, Elsiever.

[77] Marie-Claude Dubois, Ake Blomsterberg, “Energy saving potential

and strategies for electric lighting in future North European, low energy

office buildings: A literature review”, Energy and Buildings 43 (2011)

2572-2582, Elsiever.

[78] U.S. Department of Energy “Lighting: Development, Adoption and

Compliance Guide” Building technologies program, (September 2012)

[79] Monceft Krarti, Paul M. Erickson, Timothy C. Hillman, “A simplified

method to estimate energy savings of artificial lighting use from

daylighting”, Building and Environment 40 (2005) 747-754, Elsiever

[80] Francis M. Rubinstein and Mahmut Karayel, “The measured energy

savings from two lighting control strategies”, IEEE Transactions on

industry application Vol. IA-20 No. 5 September/October 1984.

[81] Jiaming Li, Josh Wall, Glenn Platt, “Indoor air quality control of

HVAC system”, Proceedings of the 2010 International Conference on

Modelling, Identification and Control, (2010)

[82] Siemens, “Demand-controlled ventilation: Control strategy and

applications for energy-efficient operation”, (2013).

[83] U.S. Department of Energy, “Building Technologies Program:

Demand Control Ventilation”, (2012).

[84] Mattias Gruber, Anders Truschel, Jan-Olof Dalenback, “𝐶𝑂2 sensors

for occupancy estimations: Potential in building automation applications”,

Energy and Buildings 84 (2014) 548-556, Elsiever

[85] N. Van Den Bossche, A. Janssens, N. Haijmans, P. Wouters,

“Performance Evaluation of Humidity-Controlled Ventilation Strategies in

Residential Buildings”, ASHRAE, (2007).

Bibliography

165

[86] Michele Vio, “L’importanza del free-cooling negli edifici per la

climatizzazione sostenibile” Available Online.

[87] Bernt Meerbeek, Marije te Kulve, Tommaso Gritti, Marielle Aarts,

Evert van Loenen, Emile Aarts, “Building automation and perceived

control: A field study on motorized exterior blinds in Dutch offices”,

Building and Environment 79 (2014) 66-77, Elsiever

[88] So Young Koo, Myoung Souk Yeo, Kwang Woo Kim, “Automated

blind control to maximize the benefits of daylight in buildings”, Building

and Environment 45 (2010) 1508-1520, Elsiever

[89] Ying-Chieh Chan, Athanosios Tzempelikos, “Efficient venetian blind

control strategies considering daylight utilization and glare protection”,

Solar Energy 98 (2013) 241-254, Elsiever.

[90] Catalogo generale pompe di calore Baltur, available online

http://www.baltur.it/.

[91] E. Molenbroek, E. Stricker, T. Boermans, “Primary energy factors for

electricity in buildings: Toward a flexible electricity supply”, Ecofys

(2011)

[92] Department of Energy and Climate Change, “Climate Change

Agreements: Conversion factors and procedures” (2008),

[93] EPA, “ENERGY STAR Performance Ratings: Methodology for

Incorporating Source Energy Use”.

[94] Prezziario regione Piemonte 2014, sez. 03 Bioedilizia, available

online http://www.regione.piemonte.it/oopp/prezzario/.

[95] Philips, “Lampade ed accessori 2014”, (April 2014)

[96] Prezziario delle opere pubbliche regione Lombardia 2011 available

online http://www.trasporti.regione.lombardia.it/.