S.1.b Building Energy Pre Certification Service

Servizio di Pre-Certificazione della Prestazione Energetica degli Edifici a Scala Urbana proposto nell’ambito del Progetto Sunshine Building Energy Pre-Certification Service at Urban Scale proposed in the context of Project Sunshine MARCO ZUPPIROLI1,C – PIERGIORGIO CIPRIANO2 – LUCA GIOVANNINI2

MARCO BERTI3 – UMBERTO DI STASO3

1 Dipartimento di Architettura, Università degli Studi di Ferrara, Italy 2 Sinergis Srl - Trento, Italy 3 Fondazione Graphitech - Trento, Italy c Corresponding author: [email protected]

RIASSUNTO.

Nel 2012 il Comune di Ferrara ha firmato il Patto dei Sindaci, il principale movi-mento europeo che coinvolge le autorità locali per ridurre le emissioni di gas serra entro il 2020 attraverso l’aumento dell’efficienza energetica e l’utilizzo di energie rinnovabili. L’obiettivo di riduzione dei gas serra definito dalla Municipalità di Ferrara è del 25%, con un risparmio pari a due terzi delle emissioni degli edifici. Una delle azioni del PAES è legata al progetto SUNSHINE (Smart UrbaN ServIces for Higher eNergy Efficiency, www.sunshineproject.eu), finalizzato all’attuazione di un servizio di pre-certificazione della prestazione energetica degli edifici basato su informazioni geografiche aperte e messe a disposizione da fonti istituzionali.

I Dipartimenti del Comune di Ferrara sono stati significativamente coinvolti nella modellazione e nella creazione di un archivio pubblico di informazioni geografiche sulle prestazioni energetiche degli edifici basato sul progetto “City GML Energy ADE”.

Il servizio di pre-certificazione proposto da SUNSHINE è un processo automatico volto ad una stima veloce della prestazione energetica degli edifici alla scala urbana, at-traverso proprietà geografiche-geometriche, fisiche e termiche degli edifici.

Per eseguire in modo efficiente il servizio è stata inoltre predisposta un’applicazione mobile (Map4Data) mirata al controllo sul campo della correttezza e della completezza delle informazioni, quali: età di costruzione, usi, altezze, numero di piani, ecc..

mailto:[email protected]

Servizio di Pre-Certificazione della Prestazione Energetica degli Edifici a Scala Urbana proposto nell’ambito del Progetto Sunshine

SUMMARY.

In 2012 the Municipality of Ferrara, Italy, signed up to the Covenant of Mayors, the mainstream European movement involving local authorities to reduce greenhouse gas emissions (GHG) by 2020 by increasing energy efficiency and through the use of renewable energy sources. The GHG target reduction defined in Ferrara is 25%, with two thirds of the saved emissions from buildings. One of the actions of the CoM Sus-tainable Energy Action Plan (SEAP) is related to the SUNSHINE project (Smart UrbaN ServIces for Higher eNergy Efficiency, www.sunshineproject.eu) for the implementation of a buildings energy pre-certification service, using open geodata already available from authoritative sources.

The Municipal Departments have been deeply involved in the modelling and crea-tion of a public repository of detailed geodata on building energy performance, based on the draft “CityGML Energy ADE”.

The building energy pre-certification service is an automatic process to rapidly es-timate the energy performance of buildings at large-scale, using geographical - geomet-ric, physical and thermal properties of buildings.

To efficiently run the service, a dedicated mobile application (Map4Data) was im-plemented to allow professional users to check “on-the-field” the correctness and com-pleteness of buildings’ geodata properties (e.g. age of construction, uses, heights, floors, etc.).

Parole chiave: edifici storici, efficienza energetica, applicazione mobile, INSPIRE.

Key words: historic buildings, energy efficiency, mobile application, INSPIRE

1. INTRODUCTION

One of the main objectives of the SUNSHINE project is the estimation of energy performance of residential buildings, at urban scale. Buildings represent about 40% of the whole final energy consumption (European Union, 2010): in 2009 European house-holds were responsible for 68% of the total energy used in buildings, mainly for space heating and domestic hot water (BPIE, 2011). In this context, buildings have to be viewed as a dynamic (the use of the building can change overtime) source of information (including their internal equipment) as a whole.

Buildings are indeed one of the main CO2 emission sources to be considered by Municipalities and other Public Authorities aiming to reduce the overall amount of ener-gy needed at urban level. The European-wide approach of the SUNSHINE project is re-lated to the following two main European policies:

- Energy Performance of Buildings Directive (EPBD): the Directive 2010/31/EU (recast) is a key regulatory instrument, which is meant to boost the energy per-formance of the building sector.

- The Covenant of Mayors: it was launched by the European Commission, after the adoption of the EU Climate and Energy Package in 2008, to endorse and

support the efforts deployed by local authorities in the implementation of sus-tainable energy policies.

The key document of the Covenant of Mayors is represented by the Sustainable Energy Action Plan (SEAP) where the signatory defines its commitment to reduce CO2 emissions by 2020. Signatories are committed to submit an “Implementation Report” every second year following the submission of the SEAP “for evaluation, monitoring and verification purposes”1.

One of the purposes of SUNSHINE project is to provide methods to implement au-tomatically large-scale assessment of building energy behaviour based on geodata avail-able from public registers (e.g. topographic maps, cadastre, building permits, energy cer-tificates, etc.) together with buildings’ properties derived from other sources (other ar-chives available in other local or national organisation, or information collected through crowdsourcing technologies). The energy performance of the built environment, the formalization of a correct framework of needs and the actual saving chances, have long been one of the most debated issues both within the industrial market and within the housing market.

The interest in energy policy and requalification of building stock, in the context of strategic planning at different scales, has attracted significant research interests aimed at combining, on the one hand, previous experience in assessing the needs of existing buildings and, on the other hand, refined urban survey tools.

The output of this kind of research is almost always the “energy-map” designed to characterise the energy performance at the urban scale. As described in (CHPA, 2014), an energy map might be used in a variety of ways and applications:

- district heating network: a map might reveal an opportunity to create a district heating network as part of a regeneration scheme;

- energy strategy: a map could form the starting point for the energy strategy for a development by identifying energy options;

- identifying energy solutions: a map may be used by a registered social landlord (RSL) to identify likely energy solutions for clusters of poorly-insulated and hard-to-treat properties;

- priority projects: the map might point to possible investment opportunities for a project developer;

- carbon compliance/allowable solutions: the map can highlight nearby energy opportunities that could help a developer meet their carbon compliance or al-lowable solution obligations under building regulations;

- inform growth options: energy maps provide information that can help decide the allocation of development sites.

2. ENERGY MAPS: STATE OF THE ART

With the aim of guiding the design of a specific energy assessment method to pro-cess eco-maps, the following analysis of the state of the art will show the strengths and

1 European Union (EU). 2010. How to develop a Sustainable Energy Action Plan (SEAP) - Guidebook. From: http://www.eumayors.eu/IMG/pdf/seap_guidelines_en.pdf


weaknesses of some of the most representative methods, analytically selected from broad international scientific literature on this issue. The analysis aims to highlight both the applicability of the selected methods and the cost-benefit ratio. As highlighted by Meyar-Naimi and Vaez-Zadeh (Meyar-Naimi & Vaez-Zadeh, 2012), energy policies, and in particular the choices aimed at encouraging sustainable development, are based on the analysis of the features of the involved area.

The selection of survey tools depends on the size of the administration (European Community, State, Region, Municipality) that must identify both the survey action plan to implement and the accuracy of the data, usually related to energy consumption ex-pressed in TOE (tons of oil equivalent). The criteria defined by the energy assessment method determine the accuracy of the level of knowledge required and, consequently, the possibility of evaluating the effectiveness of the method itself. Therefore, these crite-ria must be identified in relation to the scale of investigation.

In case of investigations related to individual devices or to specific sectors, energy indicators are identified in relation to each product or manufacturing process. These benchmarks are multiplied by the number of distributed products or implemented pro-cesses. For example, the energy consumption of washing machines is given by the ener-gy needs of a single washing machine by type and year of production multiplied by the number of washing machines sold.

Thus, we can easily switch between the micro-scale, concerning the individual de-vice, and the macro-scale, designed to facilitate energy policies. For land-use planning, and in general for the building sector, it is not possible to adopt the same simplification of benchmarking because the characteristics that affect the energy consumption of the individual device (in this case, the building) are independent variables of both the build-ing, the energy devices found inside, and usage.

In addition, building stock, unlike consumer products, is for the most part already in existence, and new ones, on which one can set policies to improve energy efficiency, are part of a building environment, national or local, that already has its own energy out-line. The individual buildings or flats vary too much in relation to one another in terms of size, construction features and devices, methods of use, age, climate and geographic environment, etc..

In scientific literature, it is possible to recognize three scales: - the global scale (macro-scale), with variables related to aggregate values to

measure energy or environmental quantities, in reference to the planet, conti-nents (Europe, North America, South America, etc.), states or regions. At this scale the data are typically aggregated and approximate because the aim is to identify the order of magnitude (Linderberger & Kummel, 2011 and Fuinhas & Marques, 2012);

- the scale of detail (micro-scale), with orders of magnitudes that refer to the in-dividual components and devices, light bulbs, appliances, automobiles, individ-ual buildings or groups of buildings, etc. At this scale, the data refer to the spe-cific object with a high level of accuracy. For buildings, the detailed methods are designed to study the energy behaviour of individual buildings (Crawley, 2008), including the assessment of costs and benefits;

- the meso-scale, which is typically an intermediate level at the regional or local scale, for which the data can be aggregate or not.

Meso-scale is a term used in meteorology. Meso-scale meteorology is the study of weather systems smaller than synoptic scale systems but larger than micro-scale and storm-scale cumulus systems. Horizontal dimensions generally range from around 5 kil-ometres to several hundred kilometres. Examples of meso-scale weather systems are sea breezes, squall lines, and meso-scale convective complexes.

This concept may be used in energy assessment methods since the phenomena that affect the surveying and measuring of energy performance indicators require a degree of study, interpretation and connection between the macro-scale (geographic environment, climate, social and economic context, etc.) and the micro-scale (building construction features, devices, methods of use, age, etc.).

For micro-scale modelling, the information needed requires a considerable level of detail and a more in-depth analysis of the building or buildings investigated. The cost increase of the implementation and the consequent decrease of the investigation area, make too-detailed methods essentially unusable. It is necessary, on the one hand, to fo-cus the aim of the investigation and, on the other hand, to design an assessment method that allows sustainable times and costs and includes the whole field of study.

Referring to the buildings in line with the method to be used, the input data may re-late the energy consumption provided by distributors, the energy performance certifi-cates provided by local governments, the thermal, physical and geometrical characteris-tics of buildings and, finally, the building systems, taking into account user habits, geo-graphic environment and local climate.

Several studies have focused their research field at the meso-scale level, with the intention of defining the energy performance of large, medium and small building stocks and to process the local energy map.

2.1 Top-down and bottom-up methods In general, there are two large families of methods: top-down and bottom-up. Fol-

lowing an examination of numerous contributions, it is possible to propose some basic concepts to clarify their meaning2. First, assessment is applied to an object of investiga-tion, whether a group of buildings, a city, a territory, which in turn is composed of more elements distinguishable from one another. Second, it is necessary to distinguish be-tween a sample and a datum.

The sample identifies the critical set of individual elements that are directly affect-ed by the assessments. The sample may consist of buildings, but also territorial areas if the object of the assessment is, for example, the region or the country.

With respect to the object of the investigation, in turn, the sample can be studied: - over an extended basis, where the study concerns the whole object, in other

words, when the sample coincides with all the individual elements included within the object itself (for example, all the buildings of a specific area);

2 The identification of these categories is discussed in-depth in Swan & Ugursal, 2009 and in Kavgic et al., 2010. For more information, please refer to Ambrogio & Zuppiroli, 2013.


- over a partial basis, where the study concerns only a part of the entire object, thus only some of the elements and not all of them, out of all those included within the object itself (for example, only one part of the buildings of a specific area, not all of them). Such a sample may be called “random”, where the ele-ments are selected on a chance basis; or “statistical”, where selection is made on a representative base.

The datum, instead, expresses the known or observed information, in connection with the elements that constitute the sample. The datum, in turn, can be:

- aggregate, if it expresses a distinctive feature of a group of multiple elements, expressed in a unified manner, or as a summation (for example, the total energy consumption of all buildings in a certain zone expressed as kWh/year); or as a mean (for example, the mean of the performance index expressed in kWh/m2*year); or as another representative parameter (for example, the effi-ciency of the systems, the transmittance of building closures, etc.). The aggre-gate datum can be obtained from non-aggregate information on all the elements, or only on a part of them, or it can be determined through a different type of cri-teria: analysis of a statistical sample of elements, inferential analysis, etc.).

- non-aggregate, if it expresses a characteristic related to each of the individual elements, expressed for each element (for example, the energy consumption or the EP indexes of each building, etc.);

Aggregated or non-aggregated data can, lastly, have a different degree of accuracy. With the top-down method the sample is investigated on an extended basis with

regards to the object of study. The expression top-down indicates a view from the top (top to bottom), to encompass the entire set of elements contained within the object of investigation, with respect to which it is possible to express the data in aggregated or non-aggregated manner.

Top-down information is used for strategic purposes: to direct policies of interven-tion and actions, or to verify them, or to identify a benchmarking value. Conversely, in the case of assessments with the bottom-up method, the sample is studied on a partial basis.

The expression bottom-up indicates a view from below (upwards), with a precise selection of elements within the object of investigation. In turn, the data may be aggre-gated or non-aggregated. Bottom-up type information is used for design purposes to adopt corrective or improvement measures with respect to benchmarking values.

The passage from the top-down to the bottom-up assessment, is generally used to verify whether the information concerning the object of the assessment, on the whole, can be attributed to even one part of the sample. For example, starting from the aggre-gate value of the energy consumption of a given territory (top-down), it is possible to check the consumption value of a single district or any other group of buildings (bottom-up). The passage from the bottom-up assessment to the top-down one is instead used to check whether one or more statistical samples (bottom-up) are representative of the en-tire object of the assessment (top-down).

The variables used in the top-down approach mainly include macroeconomic indi-cators, from the gross domestic product (GDP) to the percentage of persons employed, from the rate of replacement of the building stock, to the number of actually inhabited

residential units. Often, the top-down models also incorporate wholly general indicators that refer to the technological and construction features of the entire housing stock. In general, the approach endorses the idea that the global energy requirements are closely related to the volatility of these parameters (Swan & Ugursal, 2009, 1822). The models attributable to the bottom-up category, instead, use information processed at a lower scale than that of the area referred to in the assessment.

They can begin with the quantification of requirements for individual buildings chosen at random, or for appropriately selected small groups and, subsequently, extend the results obtained to the municipal, regional or even national level (Swan & Ugursal, 2009, 1822).

2.2 European Standard NEN-EN 16212:2012 Within the definition of regulations supporting the implementation of Directive

2006/32/EC (ESD) and Directive 2005/32/EC, CEN has established within the SFEM (Sector Forum Energy Management) the task forces for the sustainable use of energy “Energy management and Energy efficiency”. In particular, it created two Task Forces: TF 189 “Energy Management and related services” (secretariat UNI) and TF 190 “Ener-gy efficiency and savings calculations” (secretariat NEN) with the objective of studying the top-down and bottom-up investigation criteria to assess the efficiency of a certain technical solution or improvement intervention.

The European Standard NEN-EN 16212:2012 “Energy Efficiency and Savings Calculation, Top-down and Bottom-up Methods”, published on 1 September 2012, pro-vides a general approach for energy efficiency and energy savings calculations with top-down and bottom-up methods. The general approach is applicable for energy savings in buildings, cars, appliances, industrial processes, etc. This European Standard covers en-ergy consumption in all end-use sectors.

The standard is meant to be used for ex-post evaluations of realised savings as well as ex-ante evaluations of expected savings. This European Standard provides saving cal-culations for any period chosen. However, short data series may limit the possible peri-ods over which savings can be calculated.

The standard is not intended to be used for calculating energy savings of individual households, companies or other end-users.

In NEN-EN 16212:2012, the bottom-up methods involve a progressive approach based on three assessment levels: data retrieval (data sources), processing and documen-tation. It will be individual states that will define the minimum level of requirements to determine the characteristics of the reference building or buildings. The top-down meth-ods of calculation, instead, observe the changes in consumption and assess the sum of the amount of energy saved by sector from national data or aggregate values on a large scale.

The bottom-up methods of calculation, by observing the actions and incentive measures (facilitating measures), can assess the energy savings obtained through the ap-plication of specific EEI (Energy Efficiency Improvement), such as, for example, insula-tion measures, replacement of fixtures, improvement of generator efficiency, etc3.

3 For more information please refer to Boonekamp & De Coninck, 2011 and Cansino et al., 2011.


The top down methods, instead, allow the estimate of energy savings resulting from policy measures to be assessed, and CO2 emission reduction objectives to be checked, the amount of renewable energy sources set by Directive 2009/28/EC (Di-rective 20-20-20)4.

The methods proposed within NEN-EN 16212:2012 are released from the territori-al context and do not allow an investigation at the mesoscale. In fact, they concern the whole “universe of results” (top-down) or the whole “universe of solutions” (bottom-up).

2.3 Technology and standards to create energy maps The current availability of relevant technologies and standards has encouraged the

development of many research projects in the area of building energy performance esti-mation based on publicly available data with the aim of creating energy maps (Giovan-nini et al., 2014). The main challenge in this task is related to effectively providing data for the whole city area.

For example, building certificates, adopted by many of EU countries to describe building efficiency, can provide a very detailed insight on building energy properties, but on the other hand, these certifications are not mandatory for all the residential buildings and their availability is thus very sparse.

So, given the fact that publicly available data generally do not include all the in-formation needed for the energy performance calculation, one of the most common ap-proaches to energy map creation is to estimate the missing information in a reliable way, using the basic input data that is typically available, such as building geometry, building use, construction year, climatic zone, etc.

A solid example of this approach is described in (Nouvel et al., 2013), where the City Geography Markup Language (CityGML) standard is used to semantically describe the set of objects that compose the urban environment, a building typology database is exploited to statistically estimate the energy performance properties of buildings and, finally, an Application Domain Extensions (ADE) to the CityGML model is defined to store the estimated information for each building (Kaden & Kolbe, 2013).

A radically different approach is described in (Hay et al, 2010), where thermal im-ages acquired by airborne thermal cameras are used to measure the heating lost by build-ings via their roofs and windows and from that the energy performance of the buildings is estimated.

Both approaches have merits and deficiencies. In the former case, input data are publicly available, requiring no additional cost; however, having to rely on typological databases to estimate the most of the energy parameters yields a result that is typically not very statistically reliable at the building scale and is usually confined to residential buildings (where performance typologies are easier to define). Moreover, the overall software architecture is typically desktop based, so the access to the results is often lim-ited to a small number of users with advanced GIS skills.

Another limit is related to the dissemination and exploitation activities of the com-puted results: for performance reasons, the visualization is commonly provided via a

4 For more information please refer Boonekamp & De Coninck, 2011; P. Boonekamp, 2006 and Brou-nen et al., 2012.

conversion to KML standard, where the link between the building performance data and its geometry is color-coded in each building-style parameter and the other information stored in the starting CityGML file is lost.

3. SUNSHINE BUILDINGS ENERGY PERFORMANCE ASSESSMENT

The approach presented in this paper belongs to the typological kind, but makes an effort to reduce the common drawbacks that have been delineated. As described in more details in the following sections, our approach is in effect hybrid, leveraging on the out-comes of project TABULA-EPISCOPE (Corrado et al., 2012) but limiting the use of building parameters estimated typologically particularly for historic buildings.

In fact, in reference to the “user stories” that were outlined in the various meetings between the SUNSHINE partners, a possible approach was defined aimed at the quanti-tative assessment of useful thermal energy and primary energy demands at the meso-scale. Each method has specific input data, specific algorithms and provides the required outcomes with their level of approximation.

The level of detail of the input data, which is closely related to the type of results one wants to achieve, can vary a lot, mainly according to the cost-benefit ratio related to the individual information. In fact, there are readily available data with an excellent level of accuracy, even at a large scale, which allow specific analysis techniques and thus have a low cost-benefit ratio. More often, we find ourselves in front of available information of little use or with the difficulty of finding significantly beneficial data.

Independently from the method used, indications on the physical characteristics of housing, and those who inhabit it, will be required, as well as prior energy consumption and the diffusion of certain types of systems, and, more in general, the climactic condi-tions of the region and the socio-economic landscape of reference.

The same information can be gathered in an extensive way (top-down) or in a sta-tistical way (bottom-up) and can be provided as it was collected or partially reworked. To give just one example, an estimate of the total energy consumption for the residential sector is normally published by government sources. This type of assessment, already subject to a primary reworking, is however hardly usable, as, on the one hand, it is sig-nificantly inaccurate as it does not take into account the quantity of energy produced di-rectly on site and not declared, and, on the other, it is completely disconnected from any characterisation of the building stock.

A much more detailed source, parameterised to the individual residential unit, can be supplied by the entities that distribute energy for various uses to the end-users. Even in this case, however, the datum is not without uncertainty: the different characteristics of the buildings and the private habits of those who inhabit them, almost never allow suf-ficiently accurate parametric assessments.

More in-depth investigations could be conducted focusing not on consumption but on the typological, technological and material characteristics of the building and the sys-tems and habits of those who live in said units. In this case, the limit is given, on the one hand, by the number of cases and thus the number of residential units that can be inves-tigated, and consequently by the statistical nature of the analysis; and on the other, by the issues related to the processing of sensitive data.


It is also necessary to keep in mind that the more detailed the input information is, the more the operation of the algorithm used to manage it will be burdensome.

In this sense, in directing the choice toward a specific method of analysis, the fol-lowing aspects were taken into consideration:

- the possibility of applying the method with few variations to any geographic ar-ea. The choice itself of the pilots themselves in which to apply SUNSHINE is designed to characterise urban areas that differ greatly from one another, under both the morphological-typological profile, and the material-construction pro-file;

- the particular impact on the energy needs of some specific indicators (particu-larly in reference to the S/V ratio, the percentage of window areas and the ori-entation of surfaces in direct contact with the outside)5;

- the possibility of developing future scenarios, of highlighting intervention prior-ities and particularly of quantifying the real energy savings;

- the easy availability of cadastre information; - the availability of advanced geoprocessing tools to use not only in the final

stages of the process (preparing eco-maps) but in the initial stages as well (de-termining the S/V ratio and the orientation of outside surfaces);

- the availability of general information under the material-construction profile for the different areas considered (prior research and studies);

- the inability to access inside individual residential units object of the assessment (even in reference to a possible bottom-up approach).

The method studied will aim for the maximum possible accuracy in quantifying the useful thermal energy demands for winter heating (QH expressed in kWh), the primary energy demands (Qp,h expressed in kWh) and the relative energy performance index (EPi expressed in kWh/m2

*year), without being able to access inside individual residential units.

4. SUNSHINE BUILDINGS ENERGY PRE-CERTIFICATION SERVICE

The “Building Energy Pre-Certification Service” (BEP-CS) is an automatic process to rapidly estimate the energy performance of buildings at large-scale, using geograph-ical, physical and thermal properties of buildings, together with other parameters related to the context (e.g. climate zone, urban pattern, etc.). For modern buildings, constructed since the beginning of the twentieth century, the service uses typologies and outcomes defined by the TABULA project (http://episcope.eu/building-typology/).

5 In other words, to give an example, it is possible to look for the linear relationship between energy consumption (dependent variable available for a homogeneous sample of residential units), expressed in kWh/m2*year and the S/V ratio (independent variable identified for the same homogeneous sample) expressed in m-1 (where S, expressed in m2, is the surface that delimits outwards, that is, toward non-air-conditioned envi-ronments, the gross heated volume V in turn expressed in m3). Once each unit has been identified on a Carte-sian plane, the ratio between the two values for that homogeneous sample of residential units (assumed to be infinite) is given by the regression line. This line thus allows us to hypothesise the energy consumption (de-pendent variable) given by a specific value of the S/V ratio (independent variable).

http://episcope.eu/building-typology/

Instead, for historic buildings, characterized by spontaneous processes of continu-ous transformation (Caniggia & Maffei, 2001), the BEP-CS uses, on the one hand, a large set of substitute tools designed to understand the geometric and typological charac-teristics of each residential unit, such as: cadastral maps, characterisation of street fronts and relative percentage of openings, survey of accessible functional areas, aerial photo-graphs, etc. (top-down approach); and, on the other hand, the generalisation, by typolo-gies, of the material-construction characteristics and the information concerning the plant system, which is hard to investigate without direct access to the residential units (bottom-up approach).

The two different approaches for historical buildings stocks (top-down and bottom-up) will therefore be used in an integrated manner6.

The following section lists the specifications of the prototype with respect to hard-ware performance parameters, host parameters and installed software. The BEP-CS is divided into the following software modules, described below:

- “ETL procedures”: backend automatic procedures for transforming data provid-ed by partners, load them in the PostGIS database and generate the CityGML representation.

- “Editing App”: mobile application (client) for checking the quality and the completeness of data provided (on the field).

- “Visualization and editing services”: backend services used by the mobile ap-plication for visualizing and correcting data.

- “Processing service”: web service for calculating the estimated energy perfor-mance value, at building level, at large scale (district/urban area).

4.1. Software components In the cases of Ferrara, Trento and Lamia (three of the SUNSHINE pilot cities) ge-

odata represented buildings have been based on open data available from authoritative sources. In the case of Ferrara, for example, the footprints of buildings have been taken from the regional topographic database, available as open data with CC-BY license: http://geoportale.regione.emilia-romagna.it/it/catalogo/dati-cartografici/cartografia-di-base/database-topografico-regionale/immobili/edificato/unita-volumetrica-dbtr2013-uvl_gpg

Nevertheless, some of the required attributes were missing or needed to be checked on-site (about 5,000 buildings in the historical centre): an on-the-field campaign is orga-nized, involving few people from the local Department of Architecture of the University of Ferrara, for twenty workdays.

The staff of the Department of Architecture used smartphones and tablets to edit attributes via WFS-T service, and updates data on PostGIS database.

6 Hybrid model - Kavgic et al., 2010, 1686.


Figura 1 – Map4Data - WFS-T and data quality checks on buildings' properties.

Figura 2 – Map4Data - Status of quality check of buildings' properties in Ferrara. In aproximately 50 workhours more than 1000 buildings have been controlled:

4.1.1. Map4Data The “Map4Data” is a mobile application implemented for SUNSHINE partners to

allow them to check “on-the-field” the correctness and completeness of buildings’ geo-data properties (e.g. age of construction, uses, etc.) and let them add or modify such properties, if missing or needed. The app has been implemented using PhoneGap open source framework, in order to be installed on Android O.S. (4.1 or higher) devices.

Once the app has been installed on a tablet or smartphone, any logged-in user may: - Visualize a thematic map of buildings and check which buildings have some

missing properties; indeed, the thematic map shows buildings with a “traffic light” color code: “red” is used for buildings that have some missing infor-mation and have not yet been checked by any user, “yellow” is used for build-

ings that have been checked by the user but still have some missing infor-mation, “green” is used for buildings that have no missing information;

- Click on a polygon representing a building and get its details (attributes); - Edit attributes, adding missing values or correcting wrong ones; - Save edits. This mechanism allows to perform fast visual checks on site, to control thematic com-

pleteness and correctness of data, and provide further (missing) information, or correct inac-curate ones. The data visualized and edited are provided by view and editing backend ser-vices, available through GeoServer platform, installed on the SUNSHINE platform. The “Ed-iting service” is a standard OGC WFS-T service to perform transactional operations and let the user edit the attributes of buildings’ geodata.

4.1.2. Processing service This component represents the “core” component of the whole “Building Energy

Pre-Certification Service”. The processing service is the component responsible for the au-tomatic calculation of the pre-certification for each building. The processing service is a standard OGC WPS service to perform complex operations and calculations such as polygon overlay. The WPS standard also defines how a client can request the execution of a process, and how the output of the process is handled. It defines an interface that facilitates the pub-lishing of geospatial processes, their discovery and their binding to other processes.

WPS extends the web mapping server capabilities to provide geospatial analysis; in the case of the BEP-CS the WPS module has been designed to run on GeoServer platform and it will be completed and deployed in the final release. The WPS module will perform a chain of operations on buildings’ geodata and calculate the value of the estimated energy performance class.

4.2. Energy maps visualisation

Figura 3 – Sunshine 3D client and Energy map (Ferrara).

Energy maps are generated merging geometry LOD-1 information from the CityGML of the displayed city with the output of the energy performance estimation


procedure. More specifically, the color of each extruded KML polygon will be de-pendent on the estimated building energy class.

The reference between each building in the KML file and the corresponding build-ing in the 3D CityDB is ensured by storing the unique GML UUID of the building in the KML polygon name property.

By the use of a web service it will then be possible to retrieve the energy-related parameters corresponding to the selected object. The previous figure shows, the energy map visualizer composed by two interconnected parts:

- An HTML5 canvas based on CesiumJS that displays the WebGL virtual globe with KML energy maps, based on CityGML LOD-1 geometries, are loaded;

- A classical HTML tab, displaying the detailed energy data corresponding to the selected building. Comparisons between building energy efficiency characteris-tics can easily be performed using the “radar” diagram placed in the bottom-left part of the page. The diagram allows the comparison of the most important building proprieties between the current and the previously selected building.

For each building, the web client allows the possibility to refine the main algorithm input data, increasing the energy performance index estimation accuracy (Figure 4).

Figura 4 – Building analysis and simulation tool.

Table I – Some examples of comparison between EP regional database results and SUNSHINE data.

CONCLUSION

In this paper we presented some of the preliminary results of the SUNSHINE pro-ject. To validate the datum, we therefore propose the comparison between the energy certifications (concerned with monitoring energy improvements) obtained from the EP regional database (SACE) and the results of SUNSHINE project (Table I). After pro-cessing SACE data it has been possible to define the uncertainty of SUNSHINE meas-urements expressed as +/- 20%, at the 95% confidence level. In fact, since it is not pos-sible to determine absolute uncertainty, as the variables involved are too numerous, we prefer focusing on relative uncertainty.

At this level, what is more interesting is the assessment of the uncertainty deter-mined as a result of the simplifications made within the calculation procedure specified by the expeditious method (particularly in the acquisition of input data and the determi-nation of partial results), compared with the procedures specified by rules for energy cer-tification. In other words, it is as though we wanted to measure the same object (useful thermal energy demands in winter conditions for each residential unit), with two differ-ent measuring tools (Tool A: procedure specified by national regulations; Tool B: proce-dure specified by the proposed expeditious method).

The objective of high quality estimation of the energy performance of buildings has been greatly facilitated by the approach of on-site data quality checks. Indeed, as many authors already highlighted, open geodata may suffer from the lack of complete-ness and/or correctness and often need to be enriched. The app Map4Data presented al-lows to perform fast on-site data quality checks, with standard and interoperable OGC services, and using harmonised data (based on INSPIRE data model).

Moreover, the use of the emerging WebGL technology ensures the largest availa-ble audience in terms of devices, avoiding the development of device-dependent custom


clients for 3D city map visualization. On the side of data structure and visualization, im-provements will be focused on increasing the quality of the geometry displayed, making it possible to render buildings based on CityGML LoD-2 level of detail and on the de-velopment of more detailed building size type estimation procedures.

REFERENCES

Building Performance Institute Europe (BPIE), 2011. Europe’s Buildings under the Microscope. Retrieved June, 2015, from http://www.bpie.eu/uploads/lib/document/attachment/21/LR_EU_B_under_microscope_study.pdf .

Combined Heat and Power Association (CHPA), 2014. What are Energy maps? Retrieved June, 2015, from http://www.chpa.co.uk/what-are-energy-maps_245.html.

European Union (EU). 2010. How to develop a Sustainable Energy Action Plan (SEAP) - Guidebook. From: http://www.eumayors.eu/IMG/pdf/seap_guidelines_en.pdf

Meyar-Naimi H., Vaez-Zadeh S. 2012. Sustainable development based energy policy marking frameworks, a critical review. Energy Policy, 43, 351-361.

Linderberger D., Kummel R. 2011. Energy and state of nations. Energy, 36 (10), 6010-6018.

Fuinhas J. A., Marques A. C. 2012. Energy consumption and economic growth nexus Portugal, Italy, Greece, Spain and Turkey: An ARDL bonus test approach (1965-2009). Energy Economics, 34 (2), 511-517.

Crawley D. B. 2008. Building performance simulation: a tool for policymaking. Glasgow, Scotland: University of Strathclyde - Thesis for Ph.D. in Mechanical Engineering.

Swan L. G.., Ugursal V. I. 2009. Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and Sustainable Energy Reviews, 13, 1819-1835.

Kavgic M., Mavrogianni A., Mumovic D., Summerfield A., Stevanovic Z., Djurovic-Petrovic M. 2010. A review of bottom-up building stock models for energy consumption in the residential sector. Building and Environment, 45, 1683-1697.

Ambrogio K., Zuppiroli M. 2013. Energia e restauro. Il miglioramento dell’efficienza energetica in sistemi aggregate di edilizia pre-industriale tra istanze conservative e prestazionali. Milano: Franco Angeli.

European Parliament. 2006. Directive 2006/32/EC of the European Parliament and of the Council of April 5th, 2006, on energy end-use efficiency and energy services and repealing Council Directive 93/76/EEC.

European Parliament. 2005. Directive 2005/32/EC of the European Parliament and of the Council of July 6th, 2005, establishing a framework for the setting of ecodesign requirements for energy-using products and amending Council Directive 92/42/EEC and Directives 96/57/EC and 2000/55/EC of the European Parliament and of the Council.

Boonekamp P., De Coninck H. 2011. Energy: Inspiration for the future. Amsterdam: ECN.

Cansino J. M., Del P.Pablo-Romero M., Román R., Yñigue R. 2011. Promoting renewable energy sources for heating and cooling in EU-27 countries. Energy Policy, 39, 3803-3812.

Boonekamp P. 2006. Evaluation of methods used to determine realized energy savings. Energy Policy, 34, 3977–3992.

Brounen D., Kok N., Quigley J. M. 2012. Residential energy use and conservation: Economics and demographics. European Economic Review, 56, 931-945.

Giovannini L., Pezzi S., Di Staso U., Prandi F., De Amicis R. 2014. Large-scale Residential Energy Maps: Estimation, Validation and Visualization. DOI: 10.5220/0004997001700177. Conference: DATA 2014.

Nouvel R., Schulte C., Eicker U., Pietruschka D., Coors V. 2013. CityGML-based 3D city model for energy diagnostics and urban energy policy support. Proceedings IBPSA World 2013, 1-7.

Kaden R., Kolbe T. 2013. City-wide total energy demand estimation of buildings using semantic 3D city models and statistical data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II, 2/W1.

Hay G.. J., Hemachandran B., Chen G., Kyle C. D., 2010. HEAT - Home energy assessment technologies: a web 2.0 residential waste heat analysis using geobia and airborne thermal imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII, 4/C7.

Corrado V., Ballarini I., Corgnati S. P. 2012. National scientific report on the TABULA activities in Italy. Torino: Politecnico di Torino.

Caniggia G., Maffei G. L. 2001. Architectural composition and building typology. Firen-ze: Alinea.

AKNOWLEDGEMENTS

Project SUNSHINE has received funding from the EC, and it has been co-funded by the CIP-Pilot actions as part of the Competitiveness and innovation Framework Pro-gramme. The authors are solely responsible of this work, which does not represent the opinion of the EC. The EC is not responsible for any use that might be made of infor-mation contained in this paper.

S.1.b Building Energy Pre Certification Service

Technology

Transcript of S.1.b Building Energy Pre Certification Service