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1. 42 AMS El Consumption Heating 9 AMS El Consumption Lighting 5 AMS El Consumption KWh AMS Gas Consumption Heating AMS Gas Consumption KWh AMS PV Potential AMS Wind Potential Google Hybrid Google 5 Cons m3 3 186002 6850000 821000 1856000 gt 2 7 433000 821000 Are A 245001 433000 7 137001 245000 65001 137000 Scenario 1 Baseline Factor name Change Constant electricity price 0 Constant gas price 0 Scenario 2 Increasing prices Factor name Change Increasing electricity price 2 year Increasing gas price 2 year Scenario 3 Decreasing prices Factor name Change Decreasing electricity price 2 year Decreasing gas price 2 year gt accenture knows what the future brings different futures can mean different outcomes for plans that we make now We can test the plans we make under different future scenarios The aim is to find the most cost effective way for reducing emissions and this cost effectiveness is highly dependent on energy prices Therefore the uncertainty in energy prices is where the municipality is most interested in We create three scenarios that together represent a range of possible future energy prices Go to step 2 TRyNSFORM 2 Set Scenarios Start creating a scenario by
2. Fossil fuel favoured New scenario lt Create Description Consta nt gas price 0 Fossil fuel opposed Baseline scenano Remove Scenario 2 Increasing prices All factors Increasing electricity price Factor name Change Increasing electricity price 2 year Decreasing electricity price Constant electricity price Increasing gas price 2 year Decreasing gas price Constant gas price Interest rate Scenario 3 Decreasing prices Energy Savings heat Exchanger Increasing heat price rane Factor name Change 1 10 15 Decreasing elec price 2 year Decreasing gas price 2 year FI gt accenture municipality considers four major alternatives for transforming the area A Energy Saving B Max Renewables 3 A Window replacement Shower Heat Exchanger Pl 7 Solar PV panels roof facade Wind turbines HT LED lighting 17 w Insulation D All Electric Solar PV panels roof facade Wind turbines C City Grids District cooling grid Air source Heat Pump Aquifer thermal storage District heating grid open system FI TR NSFORM gt accenture Solar PV panels roof facade Wind turbines The municipality has provided realistic timeframes and implementation details for the
3. gt accenture The Process Guidebook is a supporting document and is still work in progress The final version will be ready at the end of the project P N pm NM dg 2 J A D _ a D PERLA a TA 8 7 dis a AYA is 4 amp M D Bw gt as 2a U uJ vC A ww Gum wW ti WE B esu O0 9 S9 P n We managed to get the current status of the city mapped in terms of energy usage because ERDF was involved as partner within TRANSFORM and GRDF was interested in following our experimentation they had a task and a purpose to help realize Grand Lyon s plans B atrice Couturier is the City Representative and Coordinator for Grand Lyon FI TR NSFORM l AUSTRIAN INSTITUTE OF TECHNOLOGY gt accenture 3 2 Zoom into Phases 2 Commitment COR DE The commitment phase is dedicated to setting targets with regard to data acquisition and appointing the responsibilities for reaching these targets to Defining responsibilities the right people It is very important to have commitment from all parties allocating process owners involved to ensure collective effort and motivation which is essential for and collecting the right obtaining the required data data and content Y Who in your city is responsible
4. l AUSTRIAN INSTITUTE OF TECHNOLOGY gt uon WP3 Objectives TRANSFORM To develop a prototype Decision Support Environment DSE which enables decision makers to evaluate the impacts of different transformation plans under varying scenarios based on open energy data In addition to the prototype DSE documentation materials are developed for dissemination of the DSE to other cities The Deliverable 3 2 contains all the developed documentation material required for replication of the Decision Support Environment in other cities The structure of the document is set up in the form of Guidebooks which can be used together or separately depending on the type of audience interested in the DSE gt accenture B uos 2 Glossary The Smart Energy City is highly energy and resource efficient and is increasingly powered by renewable energy sources it relies on integrated and resilient resource systems as well as insight driven and innovative approaches to strategic planning The application of information communication and technology are commonly a means to meet these objectives The Smart Energy City as a core to the concept of the Smart City provides its users with a livable affordable climate friendly and engaging environment that supports the needs and interests of its users and is based sustainable economy A specific subject that a city has chose
5. HG 4 DSE Full User Manual Link to the tool http sbc1 ait ac at web mfumarola dst Content Instructions for using the DSE through the user interface including a case study C l i s AUSTRIAN INSTITUTE M OF TECHNOLOGY a Pii TR NSFORM gt accenture un How to Start How to get your City Smart How to Add amp Adapt How to Use How to Understand DIU gt accenture Log In Analyze City Context Set Scenarios Allocate Measures Determine Impacts Measure Library e Factor Library Case study Glossary BH uon Contents of the User Manual Analyze City Context Set Scenarios How to get your City Smart Allocate Measures Determine Impacts e Measure Library How to Add amp Adapt Factor Library How to Use Case study How to Understand Glossary DI gt accenture m Om un HNOLO ELLA FI TR NSFORM Decision Support Environment be accessed through the internet and test accounts are available for new users that want to explore the options and get familiar with the DSE j accenture TR NSFORM MET i Login Username Password lt ecsceseees Select City 1 cM Genoa Hamburg Lyon Vienna accenture TR NSFORM Login Username Transformer A Password
6. How to Understand gt accenture DI a ie ia Allocate Measures Determine Impacts Measure Library Factor Library Glossary ees BJ TR NSFORM Case study Instructions In the following slides background information is provided about an area of Amsterdam which will serve as a case study area This background information is interspersed with specific instructions on how to move through the Decision Support Environment successfully in order to generate insightful results Current and target CO emissions a r 1 ES M CA gt ee Ww C os 2040 EM 81 74 kt year A Energy Saving B Max Renewables Window replacement Y A mS T M Shower Heat Exch nge m e anels anes Aq rene open system van Sonweg m open system gt accenture p wu Amsterdam Zuid Oost is mixed used area with low prices and little restrictions which makes it amsterdam 1 Be E C gt 4 4 2 Suitable for urban innovation and experiments e 77 lt ot en AY 2 7 Current and target 2 emissions 2012 220 PA 2 Diemen 4 774 2040 mu 81 74 kt year n 2 X X gt 4 y jo
7. Select City Amsterdam Y ox gt accenture 1 2 Access the Decision Support Environment Go to sbc1 ait ac at web mfumarola dst via Google Chrome Type Username and Password Click on the field enter your details If no login details are provided login with username test and password test Select the City City for which the scenario planning will be made Click Opens the Decision Support Environment AIT B uos Contents of the User Manual How to Start Log In Analyze City Context Set Scenarios How to get your City Smart Allocate Measures Determine Impacts Measure Library How to Add amp Adapt Factor Library How to Use Case study How to Understand Glossary AIT DI gt accenture ITUTE k m i Om gt un OL lt x ern FI TR NSFORM Decision Support Environment consist of four steps user 1 analyse the current energy performance of a city based on the available data 2 set scenarios containing assumptions about the future state of a city 3 mimic the transformation of an area by allocating measures and 4 test the local or city wide impact of sucha transformation under the various future assumptions scenarios Analyze the city context SOT scenarios 3 Allocate measures 4 Determine impact 1 Analyze City Context View and
8. Y What are realistic time frames for implementation of the transformation alternatives in the corresponding areas Y How are the impacts of the relevant measures calculated Which parameters need to be used Y What are the costs for realizing these different measures Y What are the relevant uncertainty factors that influence the decision making process Y What are realistic boundaries to the development of these uncertainty factors Y How is ensured that the simulation results provide the desired insights regarding the city s questions challenges and climate goals gt accenture gt accenture NB The Process Guidebook is a supporting document and 1 still work in progress The final version will be ready at the end of the project For the South East region of Amsterdam we have used the Decision Support Environment deliverable of WP3 within TRANSFORM to simulate the impact of several energy scenario s and test the robustness of the energy plans combination with external factors Bob Mantel is the City Coordinator of Amsterdam We wanted to be able to make scenario projections for the future energy use in the city our focal area being the district Part Dieu After the first phase of collecting data to aggregate it in the Energy Atlas we built 4 scenarios on the energy efficiency of carriers The objective was challenging because first we needed to build a methodology and this required a dedica
9. accenture BH uon 1 Analyze City Context 2 Set Scenarios 3 Allocate Measures 4 Determine Impacts Step 3 is dedicated to the design of transformation plans or measure portfolios These refer to factors that city actors do have control over Each measure portfolio contains a set of measures allocated to certain entities in the city e g buildings and to a specific time frame for implementation Measure Portfolio Overview Create Measure Portfolio Measure Portfolio Heat pumps Name the new measure portfolio and add a description Lokale wanmtevoorziening Portfolio Shower Heat Exchanger Thermal Heat Grid ua Remove Create Edit a measure portfolio Name the new measure portfolio and its description Ad d m 5 u res to t h e po rtfol IO Name District Heating extension Select measures from the dropdown list Description Either Add to portfolio or 2 Add measures to the portfolio and customize them by the edit buttons Edit Create a new measure All measures Click on measure to view the description Add to portfolio gt Delete from portfolio Instead of creating new measure portfolio an existing measure portfolio can be selected and either edited or applied via the outlined steps Create new measure Edit measure BH usos gt accenture 1 Analyze City Context 2 Set Scenarios 3 Allocate Measures 4 Determine Impacts Step 3 is dedicated to
10. eo bu a WV i Y 4393 23 r 7 eh Wii WASE 4 Hn Giving CIC UER a wi te B B B EU B V9 B B JJ Sia IN B es ww E D 3 80 ER Eroduction aei x Measure Editor Equation Final Energy Consumption Local Energy Produced by Renewables amp Transport Efficiency of Energy Note City specific variables are located in the cityvariable table in the db Their values in the cityvariablevalue table AIT fines AEN Bd ins FORM DIU gt accenture 5 2 2 Measure library KPI definition tables 2 2 cost public trasfrm meta formula A indicator varchar 7 5 nodename varchar 7 5 2 nodetype 75 amp 3 formulacomponent varchar 1500 8 trnsfrm meta formula pkey gt accenture n AIT Dd TRUNSFORM 5 2 2 Measure library measure editor tables 1 5 oo EIU D 2 AUC Watie Haat Shower Fat Enchangar ER M The name of the variables user friendly names in the GUI differ from the names that appear in the DB The names given in the db to these variables is as follows Affected Variables gt Measure Node Variable gt Building
11. installing the DSE on their own servers gt accenture l AUSTRIAN INSTITUTE i OF TECHNOLOGY 1 Introduction 1 1 Intended audience 2 Glossary 3 Hardware Requirements 4 Software Requirements 4 1 Specification 4 2 Installation 4 3 Data see attached Word document DSE Deployment Guide v1 0 docx Enabling Context Guide This document is also a supporting document for this deliverable to guide the intended audience through the city context and the enabling measures It is not part of the official deliverable but is seen by the team as Content a valuable source of information for cities which are interested in the Description of TRANSFORM adoption of WP3 results city contexts and guidance through enabling measures The document is still under revision and will be available shortly before the final project event the latest Audience Scientific community MT P eros AUSTRIAN NGIITUTE TR NSFORM gt accenture
12. 17 02 17 06 17 10 150093 15007 15 11 T amp n2 15 16 11 17 2 17 07 17 11 Emissions o a z All Electric Use logarithmic scale 1718 Baseline Energy Saving cold tid 1 B electricity id 1 gas id 1 heat id 1 Selected Ares Impact Total City Impact Monthly emissions from 2015 to 2018 400M 300M amo After step 5 wait till experiment is finished then view results Use loganthmic scale cold id 1 electricity id 1 B neat id 1 Monthly renewables from 2015 to 2018 200M 150M 100M SOM l arget OM 15 01 15 05 15 09 16 01 16 05 16 09 17 01 17 05 17 09 18 15 02 15 06 15 10 16 02 16 06 16 10 17 02 17 06 17 10 15 11 IRINA 1 16 11 17 2 17 07 17 11 Gosts o Use loganthmic scale cold id 1 B electricity 1 gas id 1 Bl heat id 1 Bl investments id 1 Monthly costs from 2015 to 2018 100M 50M M 15 01 15 0 Continue with the other measure portfolios create your custom experiment A Energy Saving C City Grids District cooling grid District heating grid gt accenture Window replacement Shower Heat Exchanger LED lighting Insulation Scenario 1 Baseline B Max Renewables Solar PV panels roof facade Wind turbines Factor name Change Constant electricity price 096 Constant gas price 096 Scenario 2
13. 7 55 1 1 1 TM 2 oO Rue Paul 1 E Rue Paul Bert 2 BIB ve img uus uiis 2 2 m E A gt i lt 5 Pla oy S 4 Mae Y 22 2 4 3 E 570 peal 4 2 RuedesRancy e pe Rue des Ram o 2 2 y m 7 S ggpnmare 5 A e ten 8 h 4 7 5 1 2 6 T f Su 2 a c d gt on E gt m j Rue Saint Antoine i 4 AN 1 3 rE Y t 4 gt d 55 ES gt yh Rue d Aun F t 4 11 5 Run Y ce pa bw Boa 40 20 TL 79 y Sx Jt 1 Maurice Fiare 5 i DAS e m 13 O J e gt E a 1126 VI 1 By GED A NEL TE ni nr 7 qur ms fiue de be Sax a is 75 gt gt CHO S lt gt mm E 7 mon f m o am an Im e m m E 7 A P gt Wir p EX di n nm de Le j din ee
14. Adapt Measure Library KPI Definition In case a user wants to go more in depth and review the assumptions behind the calculations of the four KPIs or change the values of city parameters the user is referred to the KPI Definition tab of the Measure Library A mindmap like structure gives an easy insight in the relations between variables and the mathematical relations behind can be reviewed by double clicking nodes in the mindmap Measure Editor KPI List ITUTE gt uon In case a user wants to go more in depth and review the assumptions behind the measure impact calculations the user is referred to the Measure Editor tab of the Measure Library A mindmap like structure gives an easy insight in the relations between variables and the mathematical relations behind can be reviewed and modified by double clicking nodes in the mindmap A user can also create new measures by itself using the Measure Editor interface Measure List um Create a measure ae aa ERN EISE TENURE Name the measure and add a Remove Shower Heat Exchanger m descri ption Solar PV panels Wind turbines Window replacement F ENETTUID Review Edit a measure Visualize an existing measure Double click on existing nodes to view and 7 Investment Cost
15. Constant gas price 0 roof facade Scenario 2 Increasing prices Factor name Change Wind turbines Increasing electricity price 2 year 5 as 29 ncreasing gas price year Air source Heat Pump Scenario 3 Decreasing prices Factor name Change Decreasing elec price 2 year storage open system Decreasing gas price 2 year Tm T m Go to step 4 Continue with initiating simulation runs and obtaining results X DTU k gt accenture TR NSFORM 4 Determine Impact 1 Analyze City Context 2 Set Scenarios 3 Allocate Measures etermine impacts 1 Analyze the city context 2 Set scenarios 3 Allocate measures EET I TN Measure library Factor library m Create experiment Geographical Data Scenarios Measure portfolios Click an item in the legend to add remove data from the graphs Measure Porti eee Baseline All IR Mm cold id 1 B electricity id 1 gas id 1 B heat id 1 Decreasing prices Monthly consumption from 2015 to 2018 1 000M Scenario description Measure portfolio description Click on scenaro fo view There desenption of ite descnption ihis measure portfolio Start Simulation Date d Simulation Date m EU 01 15 500M January 2015 250M 18 01 15 05 15 09 16 01 16 05 16 09 17 01 17 05 17 09 18 15 02 15 06 15 10 16 02 16 06 16 10
16. Increasing prices Factor name Change Increasing electricity price 2 year Increasing gas price 2 year Scenario 3 Decreasing prices Factor name Change Decreasing electricity price 2 year Decreasing gas price 2 year Contents of the User Manual How to Start Log In Analyze City Context Set Scenarios How to get your City Smart Allocate Measures Determine Impacts Measure Library How to Add amp Adapt Factor Library How to Use Case study How to Understand err TR NSFORM gt accenture How to Understand Viewing of the city specific data that describes the state of the city on the specified Analyze City Data City Data ati KPI s Viewing of the city specific data in a maps functionality with the option to select via freehand polygon specific areas i A potential future state of a city described through set of factors e g population 2 Set Scenarios Scenario T S gas price electricity price economic conditions C An independent market that provides the context for any future city transformation plans e g gas price oil price population growth 3 Allocate Measures Measure Specified interventions that are applied by stakeholders to a city Enabler Technique of method that supports the implementation effectiveness of a measure Portfolio A set of measures each allocated to a certai
17. analyze the current situation of the city and set targets for the future 2 Set Scenarios Determine the future state of the city by allocating factors of uncertainty that will influence the outcomes over time 3 Allocate Measures Design transformation plans for the city via measure portfolios in certain areas and for certain time frames 4 Determine Impacts Analyze the outcomes of the experiment created in the preceding steps compare different experiments to each other to assess feasibility BH uon gt CT 1 Analyze City Context The first step is a representation of the available city data in the Decision Support Environment that can be viewed in bar charts and on a map through an interactive geographical interface This provides a clear insight in the is situation in the corresponding city and enables the user to identify areas with opportunities for improvement Next to exploring the current status of a city the user can set sustainability targets referring to the to be situation of the city C Analyze city data Analyze the city data FON Transform Dashboard Data from Transform Consumption V Use logarithmic scale r3 Constant costs Eh Costs Extended Dashboard Data from other sources Production dicen nation dex Emo Geographical Data Selection of city area Selected City Amsterdam Set targets Future city targets as a function of the
18. clicking on Create Give the scenario a name e g Baseline and start adding factors to the scenario If the factor list is empty create factors in the factor library see next slide e When yov re finished with the Baseline scenario continue with the Increasing prices and Decreasing prices scenarios and add the corresponding factors to these scenarios 1 Analyze the city context 2 5 3 Allocate measures 4 Determine impact Measure librar Factor library Scenario Overview Create Edit a scenario Scenario Name the scenario and its description Fossil fuel cen Fossil fuel opposed Baseline Hew 6 Add factors to scenario and customize them by edit button All factors Scenario Increasing electricity price Factors in this scenario Constant electricity price Decreasing electricity price Constant electricity price Increasing gas price Decreasing gas price Constant gas price Interest rate Energy Savings heat Exchanger Increasing heat price Decreasing heat prc Go to Factor library 1 10 15 lon AUSTRIAN FI TR NSFORM c gt accenture ut Factor libra ry These steps show how the factor Increasing electricity price is created Measure library Te 1 Analyze the city context 2 Set scenarios 3
19. current city data e Set targets of current city data Carbon dioxide emission reduction 20 0 Final energy consumption reduction 20 0 Increase in renewable energy sources 20 0 Energy consumption cost reduction 20 0 gt accenture BH uon 1 Analyze City Context Set Scenarios 9 Allocate Measures 4 In the second step different futures for the city can be defined with regard to the uncertain uncontrollable factors for a city actor Examples of these factors are energy prices and interest rate Scenario Overview Set Scenarios Scenario Mame p Create new scenario Test Z bbb Select Existing Scenario follow steps below to validate accuracy halla E Delete existing scenario select scenario from list and remove what happens now Create Edit a scenario T Name the scenario and its description Create New Scenario Hame New scenario Name the Scenario Add a description Description This makes the scenario traceable and explicable to others Add factors to scenario and customize them by edit button Factors E Add factors to the Scenario Select a factor fo add fhis scenano o in this scenaria i Customize by adding under which factors Add to Scenaro b the scenario will be run 1 lt Remove from Scenario Create a new factor Edit factor gt
20. for data acquisition Y Who is responsible for data integration in the tool Y Who is responsible for installation of the tool for the city Y Who are the specialists responsible for appropriate creation of scenarios and simulation runs Y Who represents the requirements and needs of the city decision makers Y How is dealt with political commitments or priorities of the city while creating scenarios and simulation runs Y Who is committed to gathering expert information on city specific measures Y Who is committed to assuring validity of the results Y Who is responsible for stakeholder management Y Who ensures the continuous flow of new information gt accenture TR NSFORM The Process Guidebook is a supporting document and is still work in progress The final version will be ready at the end of the project In order for all the plans to materialize in the city context we have reorganized the dialogue on the full energy chain We made it an open choice for stakeholders to decide where to co invest or not to invest at all in new energy efficient initiatives This helped spark cooperation on the plans for the South Eastern region of Amsterdam where local businesses hospital stadion utilities have defined a joint agenda they are committed to work on Bob Mantel is the City Coordinator of Amsterdam Elisabeth Kongsmark is the City Coordinator of Copenhagen One of the outcomes of working w
21. measures within Air source Heat Pump the All Electric portfolio sane Solar PV panels 1 July 2015 1January 2016 7096 elec kwh 1000 Air source Heat Pump 1January 2016 1 July 2016 8096 use of building office Facade PV panels 1 July 2016 1January 2017 10096 use of building office Aquifer Thermal Storage 1January 2017 1July 2017 7096 use of building office open system Wind turbines 1 July 2017 1January 2018 10096 Go to steps 3 1 3 3 B uos l AUSTRIAN INSTITUTE OF TECHNOLOGY gt accenture 1 Analyze City Context 2 Set Scenarios Allocate Measures 4 Determine Impacts 3 1 3 3 Allocate measures Create the All electric measure portfolio and start adding measures to it repeat step 3 5 for each measure Allocate the corresponding timeframes to each measure step 6 9 1 Analyze the city context 2 Set scenarios 4 Determine impact Measure library Factor library Measure Portfolio Overview Create Edit a measure portfolio the new measure portfolio and its description Description There is description of this measure portfolio e Add measures to the portfolio and cus All measures amize them by the edit buttons Measure portfolio New measure portfolio Shower Heat Exchanger LED lighting and sensors Add to 4 Delete from portf
22. period of interest e g 2013 to 2025 7 _ be 3 25 4 Output analyzer gt accenture 5 2 User Interface This part of the documentation provides an overview of the database tables and classes used to create the web user interface ELLEN accenture 5 2 1 Database structure Step 1 Analyse city context tables 1 3 E public trnsfrm meta startingconditions 4 city varchar 7 5 kpiname varchar 7 5 fuel varchar 75 value double precision unit varchar 7 5 9 trnsfrm meta startingconditions pkey City gt accenture AIT 3 f SPERANT TRYNSFORM 5 2 1 Database structure Step1 Analyse city context tables 2 3 theme varchar 7 5 dashboard varchar 7 5 name varchar 7 5 BH unit varchar 7 5 city varchar 7 5 Bj year bigint Ej components varchar 7 5 value varchar 75 source varchar 7 5 49 trnsfrm meta citylevelmultvalue pkey C l AUSTRIAN INSTITUTE OF TECHNOLOGY gt accenture TRYNSFORM__ 5 2 1 Database structure Step 1 Analyse city context tables 3 3 username varchar 7 5 city varchar 7 5 8 targetname varchar 7 5 A value double precision 29 trnsfrm meta scenariotarget pkey WB cold BB el
23. pkey ee o Selected factor description Decreasing Electricity price Decreasing Heat price Scenario Decreasing gas price 4 Remove from Scenario Create anew factor Edit factor gt accenture AIT fons AEN TRYNSFORM 5 2 1 Database structure Step 3 Allocate measures tables 1 2 sequencename varchar 7 5 JB cityname varchar 7 5 description varchar 7 5 83 id bigint 88 user name 7 5 A trnsfrm meta sequence pkey sequenceid sequence id N a measure portiolio the new measure portfolio and its description ILS Solar PV Description There is na descripban of tis portatolio 03 sequencetomeasureid bigint sequenceid bigint Qu measures tn the portfolio and customize them by the edit buttons measurename varchar 7 5 p 9 BH priority bigint Click a measure fo view te descripfon measure tyme mara nodespercentage double precision Alec Tie m 4 Create measure Edi measure gt accenture AIT OP TERINGIN TRYNSFORM sequencetomeasureid bigint startdate timestamp percentage double precision f measure boolean Allocate measure to time From Measure Efficiency Penetration rate KK Mi AUSTRIAN INSTITUTE OF TECHNOLOGY g
24. purpose as to whether they are dependent on the energy label of a building or not Most commonly and logically the building related purposes are dependent on energy label depicted in red below and the other purposes are not depicted in blue Appliances Cooking Lighting Heating tap water Showering Heating building Cooling Ventilation systems Label A B C D E F G DTU gt accenture n x FI TR NSFORM 4 4 Data enrichment 5 6 Available statistics are used to split up the energy consumption data into different purposes Different energy profiles are created for every combination of building function and energy label Building function domestic Gas consumption per purpose Electricity consumption per purpose 100 100 80 80 Cooling 60 m Showering 60 8 Heating tap water 40 Heating tap water 40 Heating building 20 Heating building 20 Lighting Cooking Cooking 096 096 C D C D E F q Energy Label Energy Label Building function hotel Gas consumption per purpose Electricity consumption per purpose 100 100 80 80 Ventilation systems 60 60 Heating tap water Cooling 40 40 Heating building Heating tap water 2076 Cooking ids E Lighting 0 0 Appliances A B C D E F G A B C D E F G Energy Label Energy Label c uc JC 6 Coc Uu J U JUTLUUSE A SI
25. the city can provide an Date opportunity for testing the Decision Support Environment but real value is captured when Coverage the complete city is covered Some buildings City district Whole city k 3 5 FI TR NSFORM DTU gt accenture 4 2 Data components Two components of city data are needed aD Building shapefiles GIS files that contain information about the shapes and location of buildings within the city See http en wikipedia org wiki Shapefile B Building attributes Properties of the buildings that can be saved in two formats Option 1 dbf Option 2 xls As columnar The dbf file and xls must both attributes for contain a unique ID column each shape where these IDs refer to the same buildings gt accenture Open data available within municipalities shp shx 5 and sbx dbf Typically available within Typically available within energy grid companies municipalities Electricity consumption Building function kWh year office house e Gas consumption Building floor area m year Construction year Othertypes of energy Energy label consumption KWh year Ownership Available within diverse institutions or not yet available e Roof area suitable for solar panels m e Underground heat storage potential depths or kWh year Wind potential X FI TR NSFORM 4 3 Data integration and aggregati
26. the design of transformation plans or measure portfolios These refer to factors that city actors do have control over Each measure portfolio contains a set of measures allocated to certain entities in the city e g buildings and to a specific time frame for implementation Measure portfolio Meas 2 An LE Baas Allocate time and penetration rate Air source Heat Pump lite Allocate Ares Solar PV panels Allocate Time Allocate Ares Select a measure and choose Allocate Time Facade PV panels Allocate Time Allocate Ares Wind turbines Allocate Time Allocate Ares Allocate start and end date for implementation of Aquifer Thermal Storage op Allocate Time Allocate Area this measure Use slider to set a penetration rate Allocate measure time Please select the time penod from date to date in which the measure has to be implemented fora TOTAL percentage given by the penetration rate The model will distribute this percentage liinearty over the complete penod on a monthly basis From 01 15 To 04 16 Total penetration rate EJ gt accenture BH uon 1 Analyze City Context 2 Set Scenarios 3 Allocate Measures 4 Determine Impacts Step 3 is dedicated to the design of transformation plans or measure portfolios These refer to factors that city actors do have
27. 5 4 2 Package overview 5 4 3 Internal data structure architecture of the DSE 5 4 4 Simulation scheduler l AUSTRIAN INSTITUTE OF TECHNOLOGY gt accenture TR NSFORM 5 1 General Architecture Database layer City context explorer Separates the databases from the user interface This allows from a flexible architecture in which the system is independent from different types of databases GIS visual component lt Creates interactive Maps gt data and geographical database These maps can be used to visualize data and for the user selections Scenario editor ee vaadin Sequence editor Sequences Measures City data Scenarios Measure editor Postare Simulation Simulation model initiator components m Instantiates for each record in the city data s s LI FE RAY Contains the standard e g house a simulation component and software components to connects it through a network to the right MB simulate producers producers In a typical simulation experiment network and consumers about 300 000 consumers are instantiated LLL ant a pauu E wm rw LLL iE T Compe ote 1h Simulation engine Using model as input the F simulation engine will calculate Vi ly 7 3 every key performance indicator for every month for the selected
28. Allocate measures 4 Determine impact TO MAR Factor Factor Name Emissions from Gas Emissions from Muclear Emissions from Chil Emissions from Solar Emissions from Wind Emissions from Central Electricity Production Emissions from Central Gas Production missions from Central Heat Production Heat fram other renewables Electricity from Hydro Efficiency of Electricity Production from Hydro Emissions from Hydro REMOVE value ot The Remove Select date fo remove o Remove Variable to link factor Initial factor value Factor Name Electricity Price Increasing electricity price 0 208 Increasing electricity price Description There no description of this factor Create Remove Add a value to the factor Increasing electricity price values n Sat 4 Remove a value of the factor Tu 10 Ss 13 14 45 16 17 48 1 R 2 sls 20 21 22 23 24 25 etu rn to step 27 8 28 0 31 1 4 1 81 71 8 TR NSFORM c gt n 1 Analyze City Context 6 Set Scenarios 3 Allocate Measures 4 Determine Impacts Make sure all three scenarios are created and filled out Scenario 1 Baseline 4 Determine impact Factor name Change Constant electricity price 0 7
29. Attribute Auxiliary Variable Purpose Node Input Variable gt Node DIU gt accenture TRONSFORM 5 2 2 Measure library measure editor tables 2 5 N node id bigint EH carrier id bigint indicator varchar 7 5 B trnsfrm meta kpinode pkey id bigint name varchar 7 5 EH cityname varchar 5 measurename varchar 7 5 I trnsfrm meta basenode pkey node id basenode id node id bigint id bigint 2 node id bigint B update id bigint ar 7 5 EH value id bigint B update type varchar 75 EH tableuistatus boolean tion id biet parent varchar 75 Ej equation id bigin 29 trnsfrm meta measurenode pkey trnsfrm meta node pk B trnsfrm meta value pkey trnsfrm meta node pkey id bigint equation varchar 1500 equation_type varchar 7 5 indicator varchar 7 5 nod varchar 7 5 i i 2 Il A aor id gue ES cityname varchar 7 5 purpose node id bigint trnsfrm meta equation pkey prey node id bigint name varchar 7 5 purpose varchar 7 5 I trnsfrm meta purposenode pkey Note the group node table is not used it was initially thought as a way to give the same equations to different nodes B s TR NSFORM DI gt accenture 5 2 2 Measure library measure editor tables 3 5
30. TRANSFORM DTU gt accenture 4 4 Data enrichment 6 6 2 Current insulation grades U values The current insulation grades or U values for every surface of a building are based on averages per construction year and climatic zone U values W m K Built before 1975 Roof 0 50 Facade 0 50 Floor 0 50 Windows 3 00 Roof 1 50 Facade 1 50 Floor 1 20 Windows 3 50 Roof 3 40 Facade 2 60 Floor 3 40 Windows 4 20 Built 1975 1990 Built 1991 2002 0 20 0 30 0 20 2 00 0 50 1 00 0 80 3 50 0 80 1 20 0 80 4 20 Cold climatic zone 0 15 0 20 0 18 1 60 Moderate climatic zone 0 40 0 50 0 50 2 00 Warm climatic zone 0 50 0 60 0 55 3 50 Built 2003 2006 0 15 0 18 0 18 1 42 0 25 0 41 0 44 1 84 0 50 0 60 0 55 3 04 Built after 2006 0 13 0 17 0 17 1 33 0 23 0 38 0 41 1 68 0 43 0 48 0 48 2 71 From http www ecofys com files files ecofys 2005 costeffectiveclimateprotectionbuildingstock pdf Building dimensions The roof and window area are calculated from the GIS shapefiles and based on average window to wall ratios for different building functions gt accenture DTI 7k i Building function Domestic Education Hotel Industry Medical Office Shop Sport Other Window to wall ratio 0 16 0 22 0 34 0 06 0 27 0 35 0 11 0 22 0 22 TRYNSFORM TRY 5
31. Variable_id CityVariable id A id bigint city name varchar 5 name varchar 7 5 variable id bigint city varchar 7 5 value varchar 5 startime timestamp cityname varchar 7 5 A measurename 75 trnsfrm meta city plcew a city wvarchar 7 5 EH id bigint EH measuredescription varchar 5 geolevelapplication varchar 5 trnsfrm meta measure pk measure id Measure id e _ id bigint measure id bigint measure node id bigint ZI trnsfrm meta measurenodetomeasure pkey Note For future update of the database is the id in the measure table the one that needs to be a PK and not the measure name field DI gt accenture _TRYNSFORM 5 2 2 Measure library measure editor tables 4 5 value_id Value id table id bigint combo id bigint value varchar 7 5 trnsfrm meta tablevalue pkey EJ units varchar 7 5 table id bigint value id bigint id bigint table id bigint 7 JJ combo id bigint amp j name varchar 75 table key id bigint key value varchar 7 5 Key id TableKey id and attribute id buildingattribute id id bigint name 75 I key id bigint attribute id bigint type varchar 7 5 trnsfrm meta buildingattribute pkey trnsfrm meta buildingattributetotablekey pkey 1 value
32. WP3 TRANSFORM Decision Support Environment 2 Enabling cities to become Smart Energy Cities Guidance for the replicable use of the model and or methodology and recommendations for further development C 3 FI TR NSFORM AUSTRIAN INSTITUTE OF TECHNOLOGY accenture zi Mi About the Structure of the Deliverables D3 1 and 03 2 The two TRANSFORM WP3 deliverables D3 1 Finalised prototype quantitative decision support model ready for replication in other cities and D3 2 Guidance for the replicable use of the model and or methodology developed in this work package and recommendations for further development aim at different audiences and have to be seen as separate documents Where D3 1 describes the tool itself D3 2 is giving advice to cities which want to adopt the DSE in order to be able to use it in the future so some content of the deliverables has been duplicated in order to serve the different audiences and should not confuse the readers of the two documents Since these are public documents the WP3 team tried to make the deliverables as consumable as possible for future external readers accenture zz ANT esc TR NSFORM Outline contents and target audience of deliverable 3 2 Process Data Input Guidebook Manual 27 Content Content Document Document about the about the data preparation input process for cities that want to use the DSE Audience Partie
33. a 2 Become a self sufficient neighborhood where energy is produced locally from renewable sources and where energy losses are minimized Go to step 1 1 ee tey WI Sonus 1 1 Analyze city data and look up different maps of the Zuid Login with the test account in the city of Amsterdam Selected City Amsterdam 4 E Set targets of current city data Carbon dioxide emission reduction 20 0 mer Final energy consumption reduction 20 0 er Increase in renewable energy sources 20 0 er Energy consumption cost reduction 20 0 i Oost area under Geographical data Measure library Factor library 4 Determine impact Analyze city data Transform Dashboard Extended Dashboard Map Control Navigate Select Info Choose number of maps Choose level ot detail v Please select Save selection Saved Choose map type lt OSM Mapnik b w OSM Publ Transp L AMS Construction Year AMS El Consumption Applicances AMS El Consumption Cooling AMS Heating AMS El Consumption Lighting 1 AMS ElL Consumption KWh AMS Gas Consumption Heating AMS Wind Potential 186002 6850000 221000 1856000 433000 821000 77 245001 433000 7 137001 245000 66001 137000 20201 66000 OpenStreetMap contributors Current and ta
34. available data Energy consumption per purpose e Current insulation grades U values Building dimensions Building attributes Construction year gt accenture Building attributes U value Roof U value Facade U value Floor U value Windows Energy consumption Electricity consumption Gas consumption Heat consumption Energy label Building function Building attributes Shapefile Floor area Building function Number of floors Energy consumption per purpose Electricity consumption for Appliances Electricity consumption for Cooking Electricity consumption for Lighting Electricity consumption for Heating building Electricity consumption for Heating tap water Electricity consumption for Showering Electricity consumption for Cooling Electricity consumption for Ventilation systems Gas consumption for Cooking Gas consumption for Heating building Gas consumption for Heating tap water Gas consumption for Showering Heat consumption for Heating building Heat consumption for Heating tap water Heat consumption for Showering 3 Building attributes Roof area Facade area Windows area i i FI TR NSFORM 4 4 Data enrichment 3 6 e Energy consumption per purpose City statistics need to be gathered about the average division of energy consumption between different purposes and how this differs between building functions Function Domestic Domes
35. control over Each measure portfolio contains a set of measures allocated to certain entities in the city e g buildings and to a specific time frame for implementation Altocate measure to area Allocate area Choose the appropriate level of detail TON 2e A block building network Select an area for implementation of the measure End the selection by double clicking on the T T 9 ll Td Choose filter criterion to select only certain z 35 ome types of buildings T pton ale 5 Press Select to confirm the selection gt accenture BH uon 1 Analyze City Context 2 Set Scenarios Allocate Measures 4 Determine Impacts In the last step the user can determine the impacts of a measure portfolio defined in step 3 givena certain scenario step 2 A combination of measure portfolio and scenario is called an experiment One experiment basically represents a possible future for the city The user can view and compare the outcomes of different experiments measured on four city KPIs Create experiment Create experiment Scenarios Measure portfolios Scenario Name Measure Portfolio New scenario New scenario Select Scenario District Heating extension Heat Pumps Lokale warmtevoorzi
36. d BUILDING In case this information is aggregated cities need to give a attributerealname i e area typeofbuilding or function from which the calculation could be disaggregated by the simulation and aggregated back again for the ouput In that case the field measurevariable needs to be put to FALSE See example below 31 32 Amsterdam gas_consumption_cooking m3 22 trnsfrm ams tmp IRUE 32 33 Amsterdam gas_consumption_heating_building m3 19 trnsfrm ams tmp IRUE 33 34 Amsterdam gas_consumption_heating_tap water m3 20 trnsfrm ams tmp s IRUE 34 35 Amsterdam gas consumption showering m3 21 trnsfrm ams tmp 35 36 Amsterdam heat_consumption_heating_building Kwh 23 trnsfrm ams tmp ER IRUE 36 37 Amsterdam heat consumption heating tap water Kwh 24 trnsfrm ams tmp icis IRUE 37 32 Amsterdam heat_consumption showering Kwh 25 trnsfrm ams tmp ER IRUE a residential trnsfrm block vie Residential a office trnsfrm block vie Office a commerce trnsfrm block vie Commerce a industrialhall trnsfrm block vie IndustrialHall a trade service trnsfrm block vie Trade Service a social trnsfrm block vie Social a culture trnsfrm block vie Culture gt DTU Re zz _TRYNSFORM id bigint factomame varchar 7 5 83 factordescription varchar 7 5 B3 city variable name varchar 7 5 user name varchar 7 5 cityname varchar 7 5 Jp trnsfrm meta factor pkey N E public trn
37. ectricity id 1 MB gas n 1 na 11 clectrichy od 1 Bl haar Monthhy consumption from 2011 to 2020 Monthly renewables from 2011 te 2020 500M set targets of current city data Carbon dioxide emission reduction 520 m i i Final energy consumption reduction 550 Increase in renewable energy sources 57 0 ee D use legarinmicscale Eilon 9 com e Energy sumption reduction 520 B cold B electron ted 0 BB gas 6 1 M n 211 Bl etectricity 1 B gas 1 hear 10 Monthly emissions from 2011 to 2020 Monthly costs from 2011 to 2020 1 5 00M ac Et 2 5 Fac 26 219 2 0G 11 01 11 02 11 03 11 04 11 05 11 08 11 01 11 02 11 03 11 04 11 05 11 06 gt accenture eros AUSTRIAN sure TRYNSFORM 5 2 1 Database structure Step 2 Set scenarios tables id bigint scenarioname varchar 7 5 description varchar 7 5 username varchar 7 5 cityname varchar 7 5 JB trnsfrm meta scenario pkey Create Edit a scenario Name the scenario and its description Lbs biri Description e id bigint Scenano Descnption scenarioid bigint factorid bigint Ada factors to scenario and customize them by edit button E Al factors Scenario 29 trnsfrm meta scenariotofactor
38. ening Select Measure Portfolio New scenario Select simulation start date and simulation end date scenaric3 New measure New scenariod New measure New measure2 Click Add experiment Test 1 As proposed New measures Test 2 The created experiment is now sent to the simulation engine and the Scenario description Measure portfolio description results are being calculated Start Simulating Cate Eni Sirmuulstinn latae 01 16 m 04 16 m Add experiment Select experiment s to view results Impact Select the experiment you want to view the results for Click Total City Impact Selected Area Impact or Impact to view the mpa og 11 actual results on KPIs changes KPIs 4 k View results in Transform Dashboard or as Geographical Data gt DIU accenture TRXNSFORM Contents of the User Manual How to Start Log In Analyze City Context Set Scenarios How to get your City Smart Allocate Measures Determine Impacts Measure Library How to Add amp Adapt Factor Library How to Use Case study How to Understand Glossary DI gt accenture RIAN INSTITUTE NOLOGY x en TR NSFORM How to Add amp
39. estamp 300 250000 50 20000 JB city 7 5 ci dm ox 15000 49 user name varchar 7 5 E si p 10000 8 varchar 75 tU EE P fuel va rchar 7 5 m 50000 2200 56 0 150 0 A timestamp timestamp measure 7 5 83 expid bigint scenarioid bigint m A sequenceid bigint B cold Bl reco 0 cas BB Hear B electricity Bll cas Hear relativechange double precision absolutechange double precision sox 20000 200 2500000 relativechangeaffected double precision 200 2000000 totalvalue double precision 1500000 50 10000 E 5 43 trnstrm_meta_measureimpactoutput_pkey 100 5000 500000 150 5 0 200 5 INSTITUTE LOGY gt accenture 5 2 1 Database structure Step 4 Determine impact tables 3 3 D sequencetomeas ureid bigint A fk geometry bigint the geom bigint 8 trnsfrm meta sequencetogeometries ME gt accenture AU OF STRIAN INSTITUTE TECHNOLOGY Y Place Guichardi 5 2 A E 1 o td Manon r lt 3 T 4 SE 40 T des EU r 2 ro d e gt wr mu xt 06 ng ya n E bor t Ar verit Hanus 3 2 T c jw 5 4 le 1 Nid E LIS 22
40. id Value id walue id bigint ZI value varchar 7 5 attribute id bigint value varchar 7 5 trnsfrm meta constantvalue pkey attribute id BuildingAttribute ld Note Building attribute value table is currently empty The values are stored now in the table city Tobuildingattribute In addition the buildingattribute table serves as a dictionary for the values in citytobuildingattribute table B i TR NSFORM DI gt accenture 5 2 2 Measure library measure editor tables 5 5 The meaning of the fields is as follows id bigint EH cityname varchar 7 5 EH attributerealname 75 unit varchar 7 5 2 attributerealname is the name of column found in the table referenced in 5 every time the name of this buildingattributeid bigint attribute is changed in the db or a different table is used This field needs to be updated tablename varchar 7 5 EB function varchar 7 5 measurevariable boolean SS ee 4 Buildingattributeid the id in the building attribute table to which the attribute realname is linked to 1 cityname the name of the city 3 unit units 5 tablename The name of a table in the public schema This table is the one that shall contain the city data if a new table is used in the db This field needs to be updated as well 6 function Sometimes the cities give aggregated information BLOCKS or disaggregate
41. ision Support Environment to be valuable to city decision makers it is important that data is available for the whole city Having limited coverage of the city can provide an opportunity for testing the Decision Support Environment but real value is captured when the complete city is covered DTU x uon gt accenture 6 2 Inputs from the testing session questionnaires 1 2 Selected comments from the city user during the testing session of the DSE in Amsterdam in February 2015 For them the DSE is e highly developed expert too e user friendly but technical knowledge required is very high Training for data entry is necessary reported 2x e useful and simple reported 2x e a good start and a way to address smart cities but the cities are not fully ready and the same goes for the tool Joined development is needed DSE Cities e very flexible outcome and results highly depend on the quality of integrated data x TR NSFORM gt accenture 6 2 Inputs from the testing session questionnaires 2 2 The users attending the DSE testing session in Amsterdam in February 2015 identified the following requirements for improvements of the tool e Aclear overview on available measures their impacts and parameter values used is needed A Data overview is required What is available or what would be needed Instead of renewables in absol
42. ith Transform partners is that we became much more aware of the importance of the political support and commitment in the context of Climate planning and transformation After the first phase of collecting data to aggregate it in the Energy Atlas we built 4 scenarios on the energy efficiency of carriers The objective was challenging because first we needed to build a methodology and this required a dedicated effort and a special skillset It was the first time we proposed energy scenarios previously we Else Kloppenborg is a senior adviser for the city of simply did not have the competency or the tooling Copenhagen B atrice Couturier is the City Representative and Coordinator for Grand Lyon BH uon DI gt accenture Grantian Once the first results from data acquisition are successful insights can be generated from the available data in the creation phase An appointed team Running the energy of specialists creates scenarios and transformation plans Simulation runs are scenarios interpreting done to answer the city s questions as specified in the context phase as the results and making objectives for the Decision Support process actionable plans Y What are the most realistic alternatives for transforming the city Y Are these alternatives applicable across the whole city or to certain areas buildings only Y Where are the high potential areas located for each of these alternatives
43. n geographic area in time forming a transformation plan for a city combination of a scenario plus its measures measure portfolio a city or city 4 Determine Impact Experiment We i area resulting in outcomes on the predetermined KPI s Measure Library Area where all measures are stored made visible and are adaptable Measure Editor Area for adapting measure in structure in values or both Affected Variable Future to be value of a building attribute Variable Current value of a building attribute Input Variable A constant value that represents an assumption parameter A node that serves as intermediate step in an equation used to simplify equations A Auxiliary Variable node that connects group of nodes to be used in another node Factor Library Area where all factors are stored made visible and are adaptable TRYNSFORM gt accenture 5 1 General architecture Technical 3 5 2 User interface Documentation 5 2 1 Database structure Step 1 Analyse city context tables Step 2 Set scenarios tables Content Step 3 Allocate measures tables Technical details Step 4 Determine impact tables regarding the development of the DSE software 5 2 2 Measure library tables 5 2 3 Factor library tables 5 3 Package Diagram 5 4 Simulation model Audience 5 4 1 Conceptual model TRANSFORM Technicians IT specialists interested in the software
44. n to focus on for the duration of the Transform project e g district heating urban refurbishment renewables smart grids etc A specified intervention applied in a district or on the city level by a stakeholder or a group of stakeholders A potential future state of a district and or city described through a set of factors e g population gas price electricity price economic conditions City specific information that describes the state of the city in accordance with the specified Key Performance Indicators KPI s l AUSTRIAN INSTITUTE EN OF TECHNOLOGY gt accenture The Process Guidebook is a supporting document and is still work in progress The final version will be ready at the end of the project Process Guidebook 3 1 The DSE Process Framework The four C s Content 3 2 Zoom into the 4 phases with true stories from TRANSFORM cities Document about the preparation process for cities that want to use the DSE Audience Parties interested in preparing the DSE for a new city gt accenture AIT B uis 4 AUSTRIAN INSTITUTE D TR NSFORM 3 1 The DSE Process Framework four C s Understanding the current state of your City Reviewing and maintaining the accuracy and relevance of the Calibration Defining responsibilities AUNING Creation Commitment scenari
45. ntains the internal representation of the data from the database it contains the following classes whose name corresponds to the data it contains AggregatedEntity e BuildingAttribute e BuildingAttributeValue e Carrier e CityVariable e ConstantValue e Entity this contains data from the city tables e Equation e Groupnode KeyValuePair KPlNode e Measure e MeasureApplication MeasureNode e MeasureUpdatableNode e Node e NodeValue e PurposeNode e TableKey e TableKeyCombination TablueValue e Value TR NSFORM DI gt accenture 5 4 4 Simulation scheduler The simulation scheduler is a java application that is continuously running and reads the experiments table in the database to check whether there is an experiment that has not been executed yet As soon as it finds an experiment that has not been executed it will simply soawn the simulation model with the appropriate sequence and scenario ID The simulation scheduler consists of 3 classes e Dataservice to read the database table containing the experiments e Scheduler the main class that runs the program that will check the table regularly e Tunnel a helper class to start a SSH tunnel programmatically D NSFOR _TRYNSFORM gt accenture Recommendations for further development 6 1 Suggested improvements Content 6 2 Inputs from the testing ses
46. olio Facade PV panels District Heating Grid Allocate measure in time Please select the time penod from date to date in which the measure has to be implemented for a TOTAL percentage given by the penetration rate The model will distribute this percentage linearty over the complete period on a monthly basis Measure Solar PV panels From Ta Penetration rate 07 01 2015 01 01 2018 From 07 15 01 16 o 2016 o Go to step 3 4 Total penetration rate Remove 1 Analyze City Context 3 4 Allocate measures to area Find the Zuid Oost area south east and select the corresponding area and filter criterion for each measure Allocate measure to area Measure LED lighting and sensors Map Control Navigat erect Info Choose number of 1234 Please select 72 Set Scenarios Allocate Measures 4 Determine Impacts Measure portfc Measure 1 Time Solar PV panels Allocate Time Facade panels Allocate Time Allocate Ares Aquifer Thermal Stor Allocate Time Allocate Ares Wind turbines Allocate Time Allocate Area 2 e ase select vil 00Se level o Save selection Saved selections WKO OPEN Choose type Geofabrik BLOCK a d E S 6 AREA SUN S 4 CUN 2o N fal dt f ew
47. on These three steps show how the data integration and aggregation process was done in the case of the city of Amsterdam Data aggregation serves to avoid privacy issues with the energy data Getting the shape files of buildings The municipality of Amsterdam has a dataset with GIS shape files and data about construction year intended use of a building etcetera basic administrational data Each building has an identification number these IDs follow a logical order through the city i e buildings next to each other have a consecutive ID gt accenture Integrating the energy consumption data The grid operator Alliander receives the basic administration data from municipality and uses a GIS program to add the most recent annual energy consumption data as extra columns to the GIS data files If there are more connections within one building the consumption data of all connections are summed The number of connections within a building is also saved as an extra column Aggregation of energy data All buildings with less than six connections are selected for privacy reasons the data of these buildings need to be aggregated A query is run in the GIS program that averages the consumption values of a group of six buildings that lay close to each other and then gives them all the same average value Groups of buildings are made based on the identification number see step 1 X
48. os interpreting the allocating process owners results and making and collecting the right actionable plans data and content a AN INSTITUTE HNOLOGY X x FI TR NSFORM gt accenture D Context The first phase serves to define the desired objectives for the Decision Support process as well as mapping the involved stakeholders and useful data sources The current status and objectives for the city are analyzed in detail to assure correct scoping of the Decision Support process and prevent asymmetries of information Understanding the current state of your City How 15 the city performing sustainability indicators How 15 the city performing with regard to policy insights from data and informed decision making Y What are the city s climate goals Y How is the achievement of these goals measured tracked presently Y What are the main challenges of the city in the energy transition process Y Which questions need to be answered by the Decision Support Tool Y Who are the relevant decision makers and what information is required for what decisions Y What are the current restrictions and barriers to informed decision making Y Are there any political commitments or priorities concerning transformational measures Y Who are the stakeholders in ownership of necessary data Y Which of these data is already openly available Y What is the current mindset of data owners with regard to open data
49. p QJ 200 m 5 1000 ft di Please continue TRYNSFORM OpenStreetMap contributors 1 1 Analyze City Context D Set 5 Allocate Measures Determine Impacts 3 4 Allocate measures to area 1 Time 2 Area Find the Zuid Oost area south east and select the Fy corresponding area and filter criterion for each measure Facade PV panels Alocsie Time Allocate Ares Aquifer Thermal Stor Allocate Time Allocate Ares Allocate measure to area Wind turbines Allocate Time Allocate Ares Measure LED lighting and sensors Map Control Navigate Select Info Choose number of maps 1 medical v frac sun max energy frac wind energy mwh gas consumption cooking ap type gas consumption heating building gas consumption heating tap water Cat gas_consumption_showering gas_kwh 3 gas_m3 heat_consumption_heating_building heat_consumption_heating_tap_water heat_consumption_showering identifica perimeter_m status un max _ ITI Billmermeet energy e 7 oner a use_of_building wur 2 Continue when done Repeat for each measure COM y n Scenarios Measure portfolio Scenario 1 Baseline All Electric Factor name Change e 5 Constant electricity price 0 Solar PV panels
50. rget CO2 emissions net 9 NT 2040 5 05 Push for Distr IStrict grids What is the most cost effective way for reducing CO2 emissions in this area of the city taking into consideration the local characteristics of the area TW t old building5 Go to step 1 2 l AUSTRIAN INSTITUTE OF TECHNOLOGY Retrofi gt accenture B uos Analyze City Context 2 Set Scenarios 3 Allocate Measures 4 Determine Impacts 1 2 Set targets Current and target CO emissions Set reduction targets for the area 2025 55 176 kt year 0 2040 1 74 kt year 1 Analyze the city context 2 Set scenarios 3 Allocate measures 4 Determine impact Measure library Factor library Selected City Amsterdam 6 Analyze city data Transform Dashboard Extended Dashboard Control Navigate Select Info Choose number of maps 1234 e Help Please select Pease select Reset Save selection Saved selections Set targets of current city data Geofabrik Carbon dioxide emission reduction i eee biw SM Publ Transp L Final energy consumption reduction Pa AMS Construction Year Increase in renewable energy sources Amsterdam 25 an AMS El Consumption Applicances tion cost reduct 2732 52 AMS El Consumption Cooling ha latin nor nts d opts
51. s interested in preparing the DSE for a new city Parties gt accenture requirements Audience responsible for the city data User Manual Instructions for using the DSE through the user interface including a case study Audience Users of the Decision Support Environment l AUSTRIAN INSTITUTE OF TECHNOLOGY v Recommendations Enablin Technical Deployment ne for Further Context Documentation Guide Development Guide 2271 Content Content Content Content Technical details Suggestions for Document about Description of regarding the improvement of the the required TRANSFORM development of the DSE software hardware for running the DSE DSE prototype city contexts and guidance through enabling measures Audience Audience Audience Audience Technicians IT Potential future Parties Scientific specialists interested developers of the DSE interested in community in the software architecture of the DSE installing the DSE on their own servers y NSFORM Table of Contents 1 Introduction 5 2 Glossary 6 3 Process Guidebook 7 16 4 Data Input Manual 17 26 4 User manual 27 62 5 Technical Documentation 63 91 6 Recommendations for further development 92 96 7 Appendix Deployment Guide Word doc 97 8 Appendix Enabling Context Guide 98 The headlines are clickable hyperlinks
52. s after Solar PV alectiicity toram piion for heating building aux ox change the equations Add new nodes by clicking on one of the colored buttons the top menu O See glossary for the meanings of the different _ types of nodes or variables B uon gt accenture In the factor library the user review and modify the assumptions about different futures of a city These assumptions are stored in factors different evolutions of variables with a high uncertainty Next to reviewing and customizing these factors a user can also create new factors itself Create a factor Factor Name v lincessnmg eectcty Select a variable to link the factor to Name the factor and add a description Remove input Vanabie Remove value the factor Variable to link factor Name Ewctrety Pree v increasing ew t Description Review Edit a factor Remove Add value to the factor as As Increasing electricity price values Select an existing factor 227 Timestamp Go Remove value of the factor Add values and corresponding dates gt ie Remove FI gt accenture Contents of the User Manual How to Start Log In Analyze City Context Set Scenarios How to get your City Smart How to Add amp Adapt
53. s of these entities The recalculation is done using a calculate that evaluates the formulas that the users input in the measures and assigns the values to the entities The calculator will eventually calculate the formulas that are made to determine the KPls which are finally outputted to the output database my D NSFOR TR NSFORM 2 accenture 5 4 2 Package overview Src nl macomi transform data data representation of the data loaded from the database Src nl macomi transform database Classes to support database loading and outputting Src nl macomi transform equations Classes to represent an equation and its components Src nl macomi transform measure Classes to represent measure Src nl macomi transform model Classes built from atomic and coupled models that is the actual representation of the simulation model Src nl macomi transform calculator Classes to evaluate equations Src nl macomi transform model data The internal data structure of the simulation model Src nl macomi transform model modelbuilder classes that use automatically generate the simulation model from input data Src nl macomi transform model utils Various utils functions that we need in other classes gt accenture _TRYNSFORM DIU 5 4 3 Internal data structure e Src nl macomi transform data This package co
54. sfrm meta factorentry factorname 7 5 ELLE NSA timestamp timestamp value double precision p A trnsfrm meta factorentry pkey ue Note Instead of the factor name as the factor entry table the factor id should be used gt accenture c X X AIT AUSTRIAN NGIITUTE 5 3 Package Diagram 1 4 C AIT AEN TRUNSFORM gt accenture sequenceportlet webui_step3 charts_windows gt accenture AIT s AUSTRIAN NGIITUTE TRYNSFORM 5 3 Package Diagram 3 4 AMT 3 4 D TRjNSFORM gt accenture 5 3 Package Diagram 4 4 C gt accenture un AMT 3 4 D TRjNSFORM 5 4 Simulation model This part of the documentation provides an overview of the simulation model its internal data structure and the simulation scheduler x AN INSTITUTE CHNOLOGY 5 gt accenture 5 4 1 Conceptual model TRANSFORM The model distinguishes consumers network and producers Consumers and producers are entities in the system that contain attributes e g consumption values At each event in time i e scenario change or measure application a recalculation will be done on the attribute
55. sion questionnaires Suggestions for improvement of the DSE prototype 6 3 Suggested improvements by the development team Audience Potential future developers of the DSE gt accenture MT P eros AUSTRIAN NGIITUTE TR NSFORM 6 1 Suggested Improvements Open and harmonise the city energy data throughout Europe The most important finding of WP3 was the need for harmonised standardised energy data preferably delivered via standard web protocols e g semantic web technologies Linked Open Data to enable the easy integration of data into applications like the DSE and comparison of measures between the cities Concerning content of the data We repeat the three critical criteria for city data to be of maximum value for and to generate the maximum value out of the Decision Support Environment Granularity Data can be available on different resolutions from a city total value to data on a building level or even address level The desired granularity for the Decision Support Environment is building level This prevents the data from being privacy sensitive but still creates maximum value for analysis of the data e Date For valuable analysis it is important that the data is up to date Preferably the latest available dataset 1 year old is selected for upload in the Decision Support Environment Data can be easily updated every year Coverage For the data and the insights from the Dec
56. t accenture SFORM_ 5 2 1 Database structure Step 4 Determine impact tables 1 3 cityname varchar 75 startdate timestamp enddate timestamp user name varchar 7 5 varchar 7 5 JP timestamp timestamp fuel 7 5 83 expid bigint scenarioid bigint sequenceid bigint value double precision 83 affectedentityvalue double precision trnsfrm meta mainkpioutput pkey gt accenture cold id 1 electricity id 1 gas id 1 B heat id 1 Monthly consumption from 2014 to 2021 T Mr a cms 14 04 14 05 14 06 4 0 Z Use logarithmic scale Emissions 200 N 4 0 cold id 1 electricity id 1 gas id 1 Bl heat id 1 Monthly emissions from 2014 to 2021 25k Ok 14 01 14 02 14 03 14 04 14 05 14 06 INSTITUTE OLOGY electricity id 1 heat id 1 Monthly renewables from 2014 to 2021 30k in i i B 4 at i i i Ok 14 01 14 02 14 03 14 04 14 05 14 06 _ Use logarithmic scale Costs electricity id 1 Bl gas id 1 heat id 1 Monthly costs from 2014 to 2021 3M 14 01 14 02 14 03 14 04 14 05 14 06 4 TR NSFORM 5 2 1 Database structure Step 4 Determine impact tables 2 3 B cold Bl Electricity B Hear E Electricity 29 startdate timestamp Bp enddate tim
57. ted effort and a special skillset It was the first time we proposed energy scenarios previously we simply did not have the competency or the tooling Now with the new stature of Lyon as metropole it is part of our direct policy therefore we have been able to attract the manpower and the competencies to bring scenario planning a step further B atrice Couturier is the City Representative and Coordinator for Grand Lyon BH uon 3 2 Zoom into Phases 4 Calibration Calibration After the first results have been obtained collaboration between specialists city stakeholders is required for reviewing and validating the results Reviewing and anes relevance and reliability of the results needs to be ensured and additionally maintaining the accuracy B and relevance of the required datasets need to be identified information Y What are the most important insights from the results for city decision makers To what extent do the results answer the city s questions as defined in phase 1 How relevant are the results for overcoming the restrictions and barriers to informed decision making Are the results reliable and valid How can the validity of the results be improved Y Which additional datasets would improve the quality of the insights from the Decision Support Tool How can these additional datasets be acquired Y What are the most important insights from
58. the results for revising the city s questions and objectives TR NSFORM gt accenture re i TRYNSFORM accenture 4 Data Input Manual Content Document about the data input requirements Audience Parties responsible for the city data accenture A 4 1 Data requirements 4 2 Data components 4 3 Data integration and aggregation 4 4 Data enrichment AUSTRIAN INSTITUTE OF TECHNOLOGY 4 1 Data requirements There are three criteria for city data to be of maximum value for and to generate the maximum value out of the Decision Support Environment e Granularity Data be available on different resolutions from a city total value to data on a building level or even address level The desired granularity for the Decision Support Environment is building level This prevents the data from being privacy sensitive but still creates maximum value for analysis of the data Date For valuable analysis it is important that the data is up to date Preferably the latest available dataset 1 year old is selected for upload in the Decision Support Environment Data can be easily updated every year Coverage For the data and the insights from TR NSFORM the Decision Support Environment to be Granularit valuable to city decision makers it is important City that data is available for the whole city Having limited coverage of
59. tic Education Education Hotel Hotel Industry Industry Medical Medical Office Office Shop Shop Sport Sport Other Other gt accenture Carrier Appliances Cooking Lighting Elec Gas Elec Gas Elec Gas Elec Gas Elec Gas Elec Gas Elec Gas Elec Gas Elec Gas 60 0 0 0 43 2 0 0 32 9 0 0 60 0 0 0 27 3 0 0 46 8 0 0 19 7 0 0 29 6 0 0 37 1 0 0 5 0 2 0 3 9 0 0 0 0 8 8 0 0 0 0 1 3 1 3 0 0 0 0 0 0 0 0 3 1 0 0 1 9 1 7 16 0 0 0 46 4 0 0 39 9 0 0 30 0 0 0 53 2 0 0 37 3 0 0 67 9 0 0 44 2 0 0 43 6 0 0 Heating building 12 0 79 0 0 0 99 6 0 0 83 6 0 0 90 0 0 0 88 0 0 0 99 9 0 0 100 0 0 0 85 3 1 7 90 8 Heating tap water 0 6 3 8 2 2 0 4 0 3 1 5 0 0 10 0 0 2 2 1 0 7 0 1 1 0 0 0 0 1 2 9 0 7 1 6 Showering Cooling 2 4 15 2 0 0 0 0 1 0 6 1 0 0 0 0 0 8 8 5 0 0 0 0 0 0 0 0 0 4 11 8 0 7 5 9 4 0 0 0 0 6 0 0 16 0 0 0 5 0 0 0 5 9 0 0 9 4 0 0 8 1 0 0 0 0 0 0 6 3 0 0 Ventilation systems 0 0 0 0 3 7 0 0 10 0 0 0 5 0 0 0 11 2 0 0 5 7 0 0 3 3 0 0 22 6 0 0 8 1 0 0 TRYNSFORM 4 4 Data enrichment 4 6 Energy consumption per purpose Assumptions need to be set about the different energy consumption
60. ute values have stacked area graphs for electricity and heat mix so one can see how the electricity and heat mix change over time e Clearer impact graphs of how much is contributed by which measure Maybe even the option to switch on and off certain measures and see what changes in the result e Abatement curves how much money per ton of CO is reduced A tutorial phase the beginning should be added e Data harmonization is needed so a data update will be possible e clear when a user has finished with a sub step gt Continue to next step indications x TR NSFORM gt accenture 6 3 Suggested Improvements by the development team The DSE development team identified at least these improvements of the tool which could not be implemented during the project itself and need to be considered for future work e factor values not interpolated yet i e do not change gradually between provided values e Experiments cannot be deleted through the UI e It should be possible to define more than one filter criterion e Instructions need to be added for hiding carriers the charts e Energy balance overview of consumption and potential after selecting an area is needed x TR NSFORM gt accenture 7 Deployment Guide Content Document about the required hardware for running the DSE Audience Parties interested in
61. y FI TR NSFORM 4 4 Data enrichment 1 6 When the data is integrated and aggregated additional effort is required for enriching the data to enable more sophisticated analysis on the impacts of measures A method was developed for converting 10 data points into 32 data points by using city statistics Building attributes Energy data kWh year GIS Shapefile Electricity consumption Construction year Gas consumption Building function Heat consumption Floor area Energy label Number of storeys Ownership gt accenture Building attributes U value roof Roof area U value facade Facade area U value floor Windows area U value windows Energy data Electricity consumption for Appliances Electricity consumption for Cooking Electricity consumption for Lighting Electricity consumption for Heating building Electricity consumption for Heating tap water Electricity consumption for Showering Electricity consumption for Cooling Electricity consumption for Ventilation systems Gas consumption for Cooking Gas consumption for Heating building Gas consumption for Heating tap water Gas consumption for Showering Heat consumption for Heating building Heat consumption for Heating tap water Heat consumption for Showering FI TR NSFORM 4 4 Data enrichment 2 6 This data enrichment method consists of three parts In each part a category of data points is estimated from

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