Prototype Quantitative Decision Support Model
Contents
1. 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 0 206 Increasing electricity price Description There is 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 L 8 10 Ss 13 14 45 16 17 48 1 R 2 moe 20 21 22 23 24 25 etu rn to step 27 8 28 0 31 1 4 1 81 71 8 TRGNSFORM gt accenture n 1 Analyze City Context B Set Scenarios 3 Allocate Measures 4 Determine Impacts Make sure all three scenarios are created and filled out Scenario 1 Baseline A Determine impact Factor name Change Constant electricity price 096 ME uu Fossil fuel favoured New scenario lt Create Description Consta nt gas price 0 Fossil fuel opposed Baseline Hew scensrio Remove Scenario 2 Increasing prices All factors Increasing electricity price Factor name Change Increasing electricity price 2 year Decreasing elec2. 186002 6850000 821000 1856000 gt 4 J 433000 821000 Are A 245001 433000 7 137001 245000 65001 137000 Scenario 1 Baseline Factor name Change Constant electricity price 0 Constant gas price 096 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 DI gt accenture No knows what the future brings and different futures 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 TRGNSFORM 2 Set Scenarios Start creating a scenario by 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 When you re finished with the Baseline scenario continue with the Increasing prices and Decreasing pric
3. AIT s AUSTRIAN NGIITUTE TRYNSFORM 5 3 Package Diagram 3 4 AMT fines 4 D TRNSFORM gt accenture 5 3 Package Diagram 4 4 C gt accenture un AIT fines 4 SPERANT 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 RIAN INSTITUTE TRONSFORM 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 attributes 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 finally outputted to the output database zd 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 ma
4. 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 12 e ase select vil 00Se level o Save selection Saved selections WKO OPEN Esai Choose map type Geofabrik v BLOCK a J E S 6 AREA SUN S 2 CUN 2o N fal x dt f a ev 0 J 200 m 5 1000 ft di Please continue x _TRYNSFORM OpenStreetMap contributors 3 1 Analyze City Context D Set Scenarios Allocate Measures 4 Determine Impacts 3 4 Allocate measures to area 1 Time 2 Area Find the Zuid Oost area south east and select the corresponding area and filter criterion for each measure Facade PV pane
5. Bl etectricity 1 B gas 1 hear d 10 Monthly emissions from 2011 to 2020 Monthly costs from 2011 to 2020 1 5 00M ac Et 2 5 BF ac 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 EE scenarioname varchar 7 5 description varchar 7 5 EE username varchar 7 5 cityname varchar 7 5 JB trnsfrm meta scenario pkey Create Edit a scenario Name the scenario and its description x 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 A trnsfrm meta scenariotofactor pkey r o Selected factor description Decreasing Electricity price Decreasing Heat price Scenario Decreasing gas price 4 Remove from Scenario Create new factor Edit tactor gt accenture AIT il AEN TRYNSFORM 5 2 1 Database structure Step3 Allocate measures tables 1 2 A sequencename varchar 7 5 JB cityname varchar 7 5 EE description varchar 7 5 83 id bigint 88 user name 7 5 A trnsfrm meta sequence pkey s
6. 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 2040 EM 81 74 kt year A Energy Saving B Max Renewables indow replacement k Sol ar PV JJ oof facad pasa eat tPump rage y ey 5 gt m or pens system E 3 gt accenture p wu x Amsterdam Zuid Oost is mixed used area with low prices and little restrictions which makes it amsterdam 1 Be gt E C gt 4 4 2 Suitable for urban innovation and experiments e 77 lt AN Sa AY 2 7 Current and target CO2 emissions 2012 220 gt 2 Diemen 4 774 2040 mu 81 74 kt year bc x 2 X gt 4 y joa 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
7. 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 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 lea dain 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 x B a TR NSFORM DI gt accenture 5 2 2 Measure library measure editor tables 3 5 Variable id2CityVariable id A id bigint city name varchar 5 name varchar 7 5 variable id bigint EE 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 dat
8. K WP3 TRANSFORM k Decision Support Environment 5 Enabling cities to become Smart Energy Cities Finalized prototype quantitative decision support model ready for replication in other cities C accenture 2 sos About the Structure of the Deliverables D3 1 and D3 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 T l I AUSTRIAN INSTITUTE TECHNOLOGY gt accenture TRGNSFORM E BED lt ppp 77225 Process of the DSE Technical Y User Manual Y Deployment Guide development documentation Content Content Content Content Description of the DSE Instructions for
9. Measure Library How to Add amp Adapt Factor Library How to Use Case study How to Understand 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 certain geographic area in time forming a transformation plan for a city I The combination of a scenario plus its measures measure portfolio on a city or city 4 Determine Impact Experiment 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 att
10. 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 B 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 k m i Om z un OL lt m Dg TR NSFORM Decision Support Environment consist of four main steps A 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 such a 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 analyze the current situation of the city and s
11. 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 Gegfabrik OSM Mapnik b w OSM Publ Transp L AMS Construction Year AMS El Consumption AMS El Consumption Cooling AMS El Consumption Heating AMS El Consumption Lighting 1 AMS El Consumption kWh AMS Gas Consumption Heating AMS Wind Potential II 186002 6850000 _ Wl 21000 1856000 Z J 433000 821000 77 245001 433000 7 137002 245000 66001 137000 20201 66000 OpenStreetMap contributors Current and target CO2 emissions 0 QN o color 2040 81 74 kt year 905 Push for District or rict 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 Wt Retrofit old buildi
12. 5 2 fuel varchar 75 value double precision unit varchar 7 5 9 trnsfrm meta startingconditions pkey Ensis siray 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 Ej 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 Step1 Analyse city context tables 3 3 username varchar 7 5 A city varchar 7 5 A targetname varchar 7 5 A value double precision 29 trnsfrm meta scenariotarget pkey WB cold M electricity B gas Gd Yi na 11 od 1 W haar Monthly consumption from 2011 to 2020 Monthly renewables from 2011 te 2020 500M set targets of current city data Carbon dioxide emission reduction 520 s i i I Final energy consumption reduction 550 Increase in renewable energy sources 57 0 ee CUseigamhmiricae Eilon com Energy sumption rn reduction 52 B cold B electron ted 0 8 gas fed i
13. 7096 use of building office open system Wind turbines 1 July 2017 1January 2018 10096 Go to steps 3 1 3 3 ZI BS 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 W s 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 portfolio 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
14. NSFORM DTU gt accenture 5 2 2 Measure library definition tables 2 2 cost public trasfrm meta formula A indicator varchar 7 5 A nodename varchar 7 5 A nodetype 75 amp 3 formulacomponent varchar 1500 A trnsfrm meta formula pkey gt accenture n B AIT erunt Dd TRUNSFORM 5 2 2 Measure library measure editor tables 1 5 oo Mem 0 EIU D 2 AUC Wak Haat Shower Fat Enchangar i 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 Attribute Auxiliary Variable Purpose Node Input Variable gt Node DIU gt accenture TRYNSFORM 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 EH M ar 7 5 EH value id bigint B update type varchar 75
15. 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 warmtevoorzieninq 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 a measure to view the description Add to portfolio Delete from portfolio Instead of creating a new measure portfolio an existing measure portfolio can be selected and either edited or applied via the outlined steps Create new measure Edit measure D TRyNSFORM gt accenture 1 Analyze City Context 2 Set Scenarios 3 Allocate Measures 4 Determine Impacts Step 3 is dedicated to the design of transformation plans or me
16. measures under multiple future demand and pricing scenarios Support for implementation plans Opportunities and impacts of measures can be viewed and analyzed at district or building levels An overview of additional and more detailed models to support planners in decision making TR NSFORM Categories by which existing tools were screened Calculation of the energy demand of the target location in terms of electric and thermal energy Basic breakdown of the calculated energy demand Energy demand Energy conversion technologies used consideration of energy conversion input and output types energy distribution networks Transport Consideration of the energy that is used in the transport sector Associated cost to energy production or energy efficiency measures implementation E g fuel prices investment cost for energy installations Energy supply Costs assessment of the impact on the environment caused by the energy systems or different energy systems scenarios Time frame The duration of the scenario that the tool could allow the user to develop Time step The time step of the calculations to determine energy demand and production Urban design The morphology of the city and the impact of different urban structures on the energy systems Geographical scope The geographical scale under which the tool could be used TRGNSFORM Scenario development T gt accenture Categories by which ex
17. of stakeholder cooperation Measuring the impact of Smart Energy City measures recommendations for further development Dissemination amp replication WP5 J Best practices data structure and replicable stakeholder process Guidance for the replicable use of the model and or methodology developed in this work package and gt accenture l i AUSTRIAN INSTITUTE TECHNOLOGY TR NSFORM 3 4 DSE development process Involving the stakeholders The DSE development process had many touch points with the city representatives One of the most important design components was to incorporate city energy themes and translate them into energy measures in the DSE The table below contains the key phases of this process WP3 Project Month Planning ma 10 12 13 15 16 18 19 21 mom Methods Exploration Conclusions amp 15 Sketch First feedback from cities From SULs Other WP s Visiting Cities amp Interviews Requirement Gathering Data Collection AMS HAM Draft Prototype Feedback from Cities WP s Fully working Prototype Further Data collection amp Measure Development K x TR NSFORM gt accenture 3 5 Design focus from key city energy themes to key energy measures Each transform city went through the process of the down selection from 80 to 3 5 themes during intake workshops The themes were deepened into measures modelled into the
18. 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 spawn 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 Tunnel a helper class to start a SSH tunnel programmatically x D NSFOR _TRYNSFORM _ gt accenture 6 Deployment Guide Content Document about the required hardware for running the DSE Audience Parties interested in 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
19. DSE Further refinement on the most attainable measures city measures given the data availability and x timeframes Thermal heat grid implementation Solar cell roll out Wind turbine roll out Energy distribution systems Retrofitting Large consumers Connect residual heat to THG Mobility Implement cold heat recevoir Coordinated Building New Build Public private buildings Integrated planning Infrastructure Renewable energy New entrepreneurship District development 3 6 Defining the energy measures using an integrated 4 step approach The measure modelling process enabled the translation of an energy theme into a detailed measure Definition Design Validation Collection of city data of research of model of measure question structure in DSE Time Key users developmentof Key users validation of Key users collection of required city Key users validation of research question and user model structure data measure in the DSE Transform development of Transform modelling of measure and Transform demonstration of Transform guidance on model structure data into DSE modelled measure in the DSE development and kick off AUSTRIAN INSTITUTE OF TECHNOLOGY accenture Z x TR NSFORM H
20. EH cityname varchar 7 5 EH attributerealname 75 EE 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 disaggregated 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
21. WA i 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 gt accenture un AIT AUSTRIAN NGIITUTE TR NSFORM How to Start How to get your City Smart How to Add amp Adapt How to Use How to Understand DTU gt accenture Log In Analyze City Context Set Scenarios Allocate Measures Determine Impacts Measure Library Factor Library Case study Glossary B Contents of the User Manual 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 m Om un HNOLO AIT TR NSFORM Decision Support Environment can 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 Login Username Password lt ecsceseees Select City v cM e os Hamburg Lyon Vienna accenture TR NSFORM AIT Login Lsername Transformer Password Select City Amsterdam Y
22. abase is the id in the measure table the one that needs to be a PK and not the measure name field DI gt accenture TR NSFORM 5 2 2 Measure library measure editor tables 4 5 value id zValue id table id bigint combo id bigint value varchar 7 5 trnsfrm meta tablevalue pkey EE units varchar 7 5 table id bigint EE value id bigint id bigint table id bigint 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 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 x B a 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
23. 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 x 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 Ta public trnsfrm meta factorentry aenean oras mcr factorname 7 5 S ELLE NSA timestamp timestamp value double precision p A trnsfrm meta factorentry pkey ue Note Instead of the factor name as a FK in the factor entry table the factor id should be used gt accenture k X AIT AUSTRIAN NGIITUTE TRNSFORM 5 3 Package Diagram 1 4 C AIT AEN TRUNSFORM gt accenture sequenceportlet webui step3 charts windows x gt accenture
24. asure 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 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 liinearty over the complete penod on a monthly basis From 01 15 To 04 16 Total penetration rate EJ gt accenture B 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
25. ating simulation runs and obtaining results X DTU k gt accenture _TRYNSFORM 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 on an item in the legend to add remove data from the graphs Measure Porti eee Baseline All Electric ees 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 a scenaro fo view There is descnption of ite descnption ihis measure portfolio Start Simulation Date d Simulation Date m EU 01 15 5 00M January 2015 250M 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 150093 15007 15 11 T amp n2 165 07 16 11 17 3 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
26. comi 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 x B o TR NSFORM DTU 5 4 3 Internal data structure e Src nl macomi transform data This package contains 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 x _TRYNSFORM DI gt accenture 5
27. ed 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 1 contains all the developed documentation material describing the process and methodology of the DSE development and all relevant technical components including step by step guidance through the DSE functionalities B gt accenture 2 Glossary The Smart Energy City is highly energy 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 on a sustainable economy A specific subject that a city has chosen 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 electric
28. equenceid sequence id N Create Edit a measure part olio name the new measure portiolio and its description ILS Solar PV Description There is descripban of Pis 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 LA m 4 Create pew measure Edi measure gt accenture AIT OP TERINGIN TRYNSFORM sequencetomeasureid bigint A startdate timestamp percentage double precision f measure boolean Allocate measure to time From Measure Efficiency Penetration rate XX AIT AUSTRIAN NGIITUTE TRYNSFORM gt accenture 5 2 1 Database structure Step4 Determine impact tables 1 3 cityname varchar 75 startdate timestamp enddate timestamp user name varchar 7 5 kpiname 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 WB electricity id 1 gas id 1 B heat id 1 Monthly consu
29. es scenarios and add the corresponding factors to these scenarios 1 Analyze the city context Gs 3 Allocate measures 4 Determine impact Measure librar Factor library Scenario Overview Create Edit a scenario Scenario Name Mame the scenario and its description Mame Fossil fual cen Fossil fuel opposed Baseline Hew scenario 6 Add factors to scenario and customize them by edit button 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 hast prc Go to Factor library 1 10 15 lon AUSTRIAN 5 c gt accenture ut Factor libra These steps show how the factor Increasing electricity price is created Measure library rossi cect wa Te 1 Analyze the city context 2 Set scenarios 3 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
30. et 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 gt accenture 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 as 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 doi deem Emo Geographical Data Selection of city area Selected City Amsterdam Set targets Future city targets as a function of the current city data e Set targets of current c
31. go more in depth and review the assumptions behind the calculations of the four 5 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 Gr Measure Editor List gt accenture ore uon In case 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 ERN EISE TENURE Name the measure and add a Remove Shower Heat Exchanger m descri ption Solar PV panels Wind turbines Window replacement F s _ p Review Edit a measure Visualize an existing measure Double click on existing nodes to view and p lt 7 Investment Costs after Solar PV panels aux alectiicity toram piion for heating building au
32. gt Ste 55 ES gt yh gt Rue d Aun F t 4 F 11 5 gt P Rue Y E bw Boa 40 20 TL 79 y Sx Jt i 1 Maurice Fiare 5 i DAS e m 13 O 3 e lt E a QA 1126 VI 1 1 By GED A NEL ay eee gt v m nr 7 qur ms fiue Ge be FZ Sax a 75 gt gt CHO S lt gt s E 7 mon f m o am an Im Ae e m m E 7 A P gt Wir p EX di n s R Le m 4 din ee R GF s bu a WV i L Y 4393 23 r 7 ec Wii WASE 4 Giving CIC S UER u b w a vi B B B SE w w Sia RV EVs ue E D 3 ER ED Eroduction aei x Measure Editor Equation Final Energy 241 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 x AIT fines TR
33. isting tools were screened Tool type Availability Energy Energy Transport Time frame Organisation link Urban design Time step GIS Interface Semi dynamic Electricity Electricity Urban Design 11 of 59 City neighborhood Global and regional Geographical Scope GIS Interface 14 of 59 ee National state regional National state regional city DR Single project investigation 47 Existing tool analysis has exposed the need for a better spatial integration of energy related measures and linking of city wide asessment of interventions with the actions on the scale of neighborhoods urban quarters TRGNSFORM gt accenture The objective of WP3 is to enable informed decision making analyzing and integrating available data and providing quantitative information in a specific spatial context of a city State of the art and the city ambition WP1 J Map comparisons of status quo using spatial data energy housing mobility etc T s AO Transformation Quantitative Smart Urban Labs agenda for a city Pyare at district level 2 decision WP4 support tool WP3 Quantitative Quantitative prediction of prediction for impact of city wide impact of strategies and or implementation measures plans in districts Developing a web based simulation tool Promoting the sharing of energy data Supporting the process
34. ity 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 B 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 this scenano o in this scenaria www i Customize by adding under which factors Add to Scenaro gt the scenario will be run 1 lt Remove from Scenario Create a new factor Edit factor gt accenture B 1 Analyze City Context 2
35. ity price economic conditions City specific information that describes the state of the city in accordance with the specified Key Performance Indicators KPI s gt accenture AIT M amana D TRYNSFORM 3 DSE development process 3 1 Content 3 2 Description of the DSE development process 3 3 3 4 3 5 3 6 Audience Parties interested in the DSE development process Why a Decision Support Environment Analysis of existing Tools WP3 embedded within the TRANSFORM program DSE development involving the stakeholders Design focus from key energy themes to key energy measures Defining the energy measures using a 4 step approach gt accenture AIT SERAIS D TR NSFORM The quantitative decision support environment enables informed decision making It simulates outcomes of energy measures and supports fact based and sustainable planning for city transformations and contains following five benefits Reliable amp effective Decision making based on reliable analyses taking all relevant city factors and analyses to increase KPIs into account Future scenarios and expected impact on KPIs are visualized sustainability in a clear overview maps statistics etc The model is accessible online and serves as an online platform Stakeholders can add data analyze data and cooperatively propose investments and develop business plans Long term cooperation bet
36. ls 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 Help medical v frac sun max energy frac wind energy mwh gas consumption cooking ap type gas consumption heating building gas consumption heating tap water gas_consumption_showering gas_kwh L ela SS gas_m3 heat consumption heating building heat consumption heating tap water heat consumption showering MA identifica name perimeter m status un max _ 1 ITI Billmermeet energy e 7 oner a use_of building wur 2 Continue when done Repeat for each measure COM v n Scenarios Measure portfolio Scenario 1 Baseline All Electric Factor name Change e 5 Constant electricity price 096 Solar PV panels Constant gas price 096 roof facade Scenario 2 Increasing prices Factor name Change Wind turbines Increasing electricity price 296 year 5 as 29 ncreasing gas price year Air source Heat Pump Scenario 3 Decreasing prices Factor name Change m s Decreasing elec price 2 year storage open system Decreasing gas price 296 year AA Tm T m Go to step 4 Continue with initi
37. mption from 2014 to 2021 s T Mr a cms 14 04 14 05 14 06 4 0 _J 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 4 at I B 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 EB ses TRYNSFORM 5 2 1 Database structure Step4 Determine impact tables 2 3 B cold Bl Electricity B Cas EB Electricity B Heat 29 startdate timestamp Bp enddate timestamp 300 250000 50 20000 A city varchar 7 5 ci dm ox 15000 49 user name varchar 7 5 E si p 10000 8 A 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 EE sequenceid bigint B cold Bl reco Bl cas Blocs B deci MB cas Il Hear relativechange double precision absolutechange double precision sox 20000 300 2500000 relativechangeaffected double precision 200 2000000 totalvalue double precisio
38. n 1500000 50 10000 43 trnsfrm meta measureimpactoutput pkey 100 5000 500000 150 5 0 200 5 INSTITUTE LOGY gt accenture 5 2 1 Database structure Step4 Determine impact tables 3 3 D sequencetomeas ureid bigint A fk geometry bigint 2 the geom bigint 8 trnsfrm meta sequencetogeometries ME gt accenture AU OF STRIAN INSTITUTE TECHNOLOGY Y Place Guichardi 5 2 A E 1 o 1 h r lt s T 4 aR SE 40 EU r 2 ro d Ee 2 wr mu xt _ s ng 2 n E bor t Ar verit Hanus 3 2 T c jw 5 4 le 1 Nid E LIS ae gee 22 7 55 1 e 1 amp 1 TM 2 o Rue Paul 1 E Rue Paul Bert 2 BIB ve img uus uiis h 2 2 m E A gt x lt 5 Pla oy Mae Y 22 2 3 E 570 peal 4 2 RuedesRancy D e r Rue des Ram o 2 2 m 7 y S pspnmare 5 e ten h 4 7 5 1 2 6 T f Su 2 a c d k on xA E gt m j Rue Saint Antoine i 4 AN 1 3 rE Y t 4
39. n Go to step 1 2 gt accenture ZI AUSTRIAN NG TUTE BS 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 Eli 55 176 kt year 0 2040 74 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 k i eee biw SM Publ Transp L Final energy consumption reduction Pa AMS Construction Year Increase in renewable energy sources Amsterdam 25 5 El Consumption Applicances E tion cost reduct 2732 52 AMS El Consumption Cooling ha ene ipic 42 AMS El Consumption Heating 9 AMS El Consumption Lighting ib ro AMS El Consumption KWh AMS Gas Consumption Heating AMS Gas Consumption KWh AMS Pv Potential AMS Wind Potential Google Hybrid Google 5 Cons m3
40. omponent lt Creates interactive Maps gt VV a 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 simulate producers producers In a typical simulation experiment network and consumers about 300 000 consumers are instantiated LLL ant a E m u w i gt Simulation engine Using model as input the F simulation engine will calculate Vi ly 7 3 every key performance indicator for a every month for the selected period of interest e g 2013 to 2025 x EE 7 TRNSFORM 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 A city varchar 7 5 A kpiname varchar 7
41. 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 15 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 03 17 07 17 11 Costs 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 or 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 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 grr ES os 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
42. ribute 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 x TRYNSFORM DI 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 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 c
43. 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 Endthe selection by double clicking on the map T T ll Choose filter criterion to select only certain 35 ome types of buildings T pton ale Press Select to confirm the selection gt accenture B 1 Analyze City Context 2 Set Scenarios 2 Allocate Measures 4 Determine Impacts In the last step the user can determine the impacts of a measure portfolio defined in step 3 given a 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 KPls Create experiment Create experiment Scenarios Measure portfolios Scenario Name Measure Portfolio New scenario New scenario Select Scenario District Heating extension Heat Pumps Lokale warmtevoorziening Select Measure Portfolio New scenario Select sim
44. tricity price Constant electricity price Increasing gas price 296 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 TRNSFORM gt accenture The municipality considers four major alternatives for transforming the area A Energy Saving B Max Renewables 4 A Window replacement N Shower Heat Exchanger Pl 7 Solar PV panels roof facade Wind turbines HT LED lighting 17 Wi 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 k TR NSFORM gt accenture Solar PV panels roof facade Wind turbines The municipality has provided realistic timeframes and implementation details for the measures within Air source Heat Pump the All Electric portfolio r 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
45. ulation 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 acc m Click Total City Impact Selected Area Impact or Impact to view the mpa og actual results on 5 changes 5 4 k View results in Transform Dashboard or as Geographical Data gt DTU 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 x RIAN INSTITUTE TR NSFORM How to Add amp Adapt Measure Library KPI Definition In case a user wants to
46. using Technical details Document about the development process the DSE through the regarding the required hardware for user interface including development of the running the DSE a case study DSE software Audience Audience Audience Audience Parties interested the Users of the Decision Technicians IT Parties interested in DSE development Support Environment specialists interested in installing the DSE on process the software their own servers architecture of the DSE gt accenture i A SERAIS UE D TRYNSFORM Table of Contents 1 Introduction 5 2 Glossary 6 3 Process within TRANSFORM 7 3 1 Why a decision Support Environment 8 9 3 2 Analysis of existing tools 10 11 3 3 Decision Support Environment embedded in TRANSFORM as WP3 12 3 4 The DSE development process Involving the stakeholders 13 3 5 Design focus from key city energy themes to key energy measures 14 3 6 Defining the energy measures using an integrated 4 step approach 15 4 User manual 16 51 5 Technical Documentation 52 80 6 Appendix Deployment Guide Word doc 81 The headlines are clickable hyperlinks gt accenture DI a shane AIT e AUSTRIAN INSTITUTE TR NSFORM 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 bas
47. ween stakeholders The model serves as a growing dynamic database data is stored and added Open data support online on a continuous basis For every data set access levels can be managed x from fully accessible to completely secured Measure definitions can be exchanged between cities to share knowledge City expertise exchange Exchanged measures can then be applied to specific local city data to ensure local applicability The model gives direct access to the right data measures scenarios and tools Cost savings This prevents the city from starting every project with new data gathering and analysis which saves project costs and time ZI st TUT BS gt accenture DSE helps in identifying opportunities allocating measures determining potential impacts and gaining stakeholder commitment Plan Implement gt Analyze the city context Set scenarios and targets Define measures Allocate measures Determine impact Analyze results Commit to implement Measure effects Support for the transformation of the city s strategic agenda Focusing on city data and insights from these data required for decision making Viewing city and district data in a spatial form for assessing the opportunities to improve Model for developing and allocating measures and viewing their impact on energy indicators Analysis of the effects of
48. x ox l change the equations ra mmm Add new nodes by clicking on one of the s colored buttons in the top menu See glossary for the meanings of the different CL Rh types of nodes or variables B usos 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 Factor Name Remove gt accenture input Vanabie Remove value the factor Variable to link factor Name Ewctrety Pree v increasing ew t Description Add value to the factor Increasing electricity price values 227 Timestamp Remove a value of the factor Remove Create a factor Select a variable to link the factor to Name the factor and add a description Review Edit a factor Select an existing factor Add values and corresponding dates TRNSFORM 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 How to Understand gt accenture DI a ie ia Allocate Measures Determine Impacts Measure Library Factor Library Glossary iis ees TR NSFORM
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