Energy use optimization in cities: Innovative technological … et... · 2015-10-13 · 8th...

8th International Scientific Conference

Energy and Climate Change Contributing to Deep Decarbonization

7 – 9 October 2015, Athens, Greece

Energy use optimization in cities: Innovative technological solutions for the local authorities

Vangelis Marinakis, Alexandra Papadopoulou, Vangelis Psarras, Haris Doukas

Decision Support Systems Lab EPU‐NTUA

Session : A2 – Energy systems analysis, scenarios

8th International Scientific Conference “Energy and Climate Change Contributing to Deep Decarbonization” 2

Contents

Introduction

Web‐based DSS

Integrated Web Portal

Comparative Analysis

Conclusions


Introduction (1/3)

EU Climate and Energy Package has set very ambitious targets for sustainable development (20‐20‐20).

“In 2006, urban areas accounted for 67 – 76 % of energy use and 71 – 76 % of energy‐related CO2 emissions.”

(IPCC, 2014)

“Cities and towns also produce over 70% of global energy‐related CO2 emissions.” (IEA, 2009)

“Cities consume as much as 80 percent of energy production worldwide and account for a roughly equal share of global greenhouse gas emissions”

(World Bank, 2010)

Urban GHGs emission facts


Introduction (2/3)

The term ”Smart City” is used to describe ICT solutions for cities and to highlight ICT importance and potential in helping the city to develop competitive advantages.

(Kramers A. et. al, 2014)

Smart Cities

City changes include:

smart transportation;

smart environment;

smart healthcare;

smart energy;

smart education;

smart safety etc.


Introduction (3/3)

Scope

Innovative technological solutions

for the local authorities that assist

them to optimize the energy use in

their premises and reduce CO2

emissions. Web‐based Decision Support

System (DSS)

Integrated Web Portal Need

Energy related data

available in the

cities

Tools and methods to achieve significant

reduction of energy consumption and CO2

emissions through the contribution of

Information and Communication

Technology (ICT).


Web‐based DSS (1/10)

• Start Date: October 2013

• Project Duration: 36 Months

• Co‐funded by the European Commission ‐ FP7

• Project partners:

OPTIMising the energy USe in cities with smart decision support systems (OPTIMUS) http://www.optimus‐smartcity.eu



Combining data for energy and infrastructure efficiency

Semantic data modeling

DSS

Multidisciplinary Data Sources

Prediction Models ‐ Inference Rules – Action Plans

DSS Innovation

8th International Scientific Conference “Energy and Climate Change Contributing to Deep Decarbonization”

Social data module

8


Multidisciplinary Data Sources

Interconnection of five heterogeneous data sources, which are used to suggest short‐term Actions Plans for public authorities with the goal of reducing energy consumption.

Weather forecasting module

De‐centralized sensor‐based module

Energy prices module Renewable energy

production module



Semantic Data Modeling

Implementation of a holistic interoperability solution using Semantic

Web technologies

Data Translation: Data from the original format into RDF according to a global ontology.

Data Communication: The RDF data are sent to the DSS using a publish‐and‐subscribe model (Ztreamy server).

Data Contextualization: The Semantic Service receives the RDF triples and processes them.

Data Storage: The Semantic Service uploads the final RDF triples on a triple store (e.g. Virtuoso server).




Forecasting the behaviour of the buildings in an accurate manner becomes necessary for the implementation of the Action Plans provided by the DSS.

RapidMiner processes running on a RapidAnalytics server.

These processes can be invoked remotely as a web service.

Prediction Models




Structured as a logical function (e.g. if/then) or as a mathematical model.

Able to describe the system (building, technical systems etc.) on the basis of an expert knowledge and provide the optimization criteria for a specific action.

Specific actions are associated to different strategies that allow optimizing, on one hand, a single indicator and, on the other hand, a combination of indicators.

Action Plans

Inference Ru

les



12



DSS Interface


DSS

Sant‐Cugat

Savona

Zaanstad

Savona, “Colombo‐Pertini School” & Savona Campus which integrates its smart grid and de‐centralized PV production into the analysis performed.

Sant Cugat, “Sant Cugat Town Hall” and “Theatre”.

Zaanstad, “Zaanstad Town Hall”.

Pilot Appraisal

15



Web Portal (1/8)

Start Date: July 2014

Project Duration: 15 Months (2 Months extension)

Project Partners:

ESCO Smart City Energy Plan (ESCOCITY) http://escocity.eu

Co‐funded by the Hellenic Republic ‐ Ministry of Culture, Education and

Religious Affairs ‐ General Secretariat for Research and Technology ‐ Special Service for Management and Action

Implementation on the fields of Research , Technological Development

and Innovation.

16


Web Portal (2/8)

Web Portal Innovation

Green Buildings (public buildings, schools, etc.);

Green Pillars/Pole (Surge protection, voltage optimization solutions, street lighting power monitoring, etc.);

Green Electric Vehicle (EV) Station Support.


Web Portal (3/8)

Mayorand advisors

Municipal technicalservices

Municipal accountingservices

Municipal Employees

Citizens/Public users

Userswith access toa profile thatincludes themonetary depictionof the energy consumptions.

Userswith limitedaccess to theWeb Portal, able to access a moredetailed levelof informationon themunicipalconsumptions.

Userswithout specificaccessto theWeb Portal, able only toaccess the public profile.

Fullaccess to the profile, althoughnot very detaileddataare expectedtobe included.

Users have fullaccess to the datamonitored, in all forms (diagrams, databases etc).

Overview of the target groups and their main characteristics


Web Portal (4/8)

Green Buildings Tool

Green Buildings Tool collects, analyzes and presents

data amongst four major groups of indicators

concerning the buildings’ energy management.


Web Portal (5/8)

Green Pillars/Pole Tool

Green Pillars/Pole Tool focuses on the street and road lighting control, by the following:

voltage control supplied to the lighting circuit;

operating lights efficiently by user defined time schedules;

collecting data on street lighting, analyzing the lamps’ failures and reports crucial data for the user.

20


Web Portal (6/8)

Green EV Station Support Tool

Green EV Station Support Tool processes data for EV charging stations, namely those parking spaces where EV supply equipment will be used to charge vehicles.

Collection of important data regarding the charging stations’ energy management.

21


Web Portal (7/8)

Web Portal Interface

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Pilot Appraisal

Moshato‐Tavros Town Hall

Nikaia‐Rentis 12th Primary School

Rhodes Street Lighting System

23

Web Portal (8/8)


Comparative Analysis

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Web‐based DSS Web Portal

Data integration process x (developed using Semantic Web technologies) x

Weather forecasting x

Social data x

Energy prices x

De‐centralized sensor‐based x x

Renewable energy production x x

Data processing x (prediction models, inference rules)

Energy management X (including alerts)

Proposed actions x

Web environment x x

Buildings x x

Lighting x

Vehicles x

RES production, storage, etc. x (linked to the buildings) x

Municipal Buildings/ Infrastructures

Data Capturing


Conclusions

Web‐based Decision Support System combines and integrates heterogeneous data sources, in order to propose specific

Action Plans to the local authorities.

http://escocity.eu

http://www.optimus‐smartcity.eu

Integrated Web Portal provides energy management services to the local authorities through a web‐based platform.

25

8th International Scientific Conference

Energy and Climate Change Contributing to Deep Decarbonization

7 – 9 October 2015, Athens, Greece

Thank you very much for your attention!

Vangelis Marinakis, Alexandra Papadopoulou, Vangelis Psarras, Haris Doukas

Decision Support Systems Lab EPU‐NTUA

Energy use optimization in cities: Innovative technological … et... · 2015-10-13 · 8th...

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