A Multi-Scale, Web-based Application for Strategic Assessment of PV

Potentials in City Quarters

Sally Köhler

, Rosanny Sihombing

, Eric Duminil

, Volker Coors

and Bastian Schröter

Center for Sustainable Energy Technology, Hochschule für Technik Stuttgart, Schellingstraße 24, Stuttgart, Germany

Center for Geodesy and Geoinformatics, Hochschule für Technik Stuttgart, Schellingstraße 24, Stuttgart, Germany

Keywords: Energy Simulation Tool, Urban Modeling Usability, Web-based Application for Energy Concepts, Feasibility

and Efficiency of PV Systems, Neighborhood Strategies, Stepless Scalability.

Abstract: This paper introduces a web-based application that visualizes building specific simulation results regarding

renewable potentials and economics for entire city quarters. Focusing on the building stock, this application

enables decision-makers to consider energy related aspects in early-stage city quarter planning. The applica-

tion builds on the existing energy simulation platform, SimStadt, which allows the detailed assessment of

buildings’ energetic performance or photovoltaic rooftop potentials based on 3D CityGML models. A new,

user-friendly and browser-based graphical user interface (GUI) makes energetic modeling more accessible

and independent of a user’s operating system. Furthermore, a customizable economic analysis was added to

the pre-existing workflow to calculate rooftop PV potentials, allowing the evaluation of renewable energy

potentials with their associated total investments or levelized cost of electricity (LCOE) at building level.

Combined, these improvements create new use cases for modeling environments previously reserved for re-

searchers, such as enabling utilities and their house-owning customers to identify PV potentials and costs, or

PV project developers to more easily and accurately locate neighborhoods with high potential. Further func-

tionalities such as building heating and cooling demand assessment will be included in a next step to extend

the scope of this application towards a versatile urban energy system simulation platform.

1 INTRODUCTION

The European Commission’s plan for a Green New

Deal proposes raising its greenhouse gas (GHG)

emissions reduction target for 2030 from 40% to 55%

compared to 1990 levels (European Commission,

2020). As buildings are responsible for about 40% of

energy consumption and 36% of CO

emissions in the

EU and are by this the single largest energy consumer

in Europe (European Commission, 2020), any reduc-

tion in GHG emissions must focus on cities, where

the use of renewable energy technologies, particularly

in the building stock, needs to be increased.

Hence, approaches and tools that simplify the en-

ergetic assessment of the building stock and that pro-

pose technologically and financially feasible options

towards sustainable city quarter planning are needed.

https://orcid.org/0000-0002-6377-9221

https://orcid.org/0000-0001-7447-5760

https://orcid.org/0000-0002-0413-8977

https://orcid.org/0000-0002-7915-1471

Such tools should feature a high level of detail regard-

ing spatial and/or temporal resolution in order to pro-

vide meaningful information for key stakeholders

such as city officials or project developers to act

upon.

A review of existing modeling approaches and

tools for energy system simulation on the scale of city

quarters are presented in (Allegrini et al., 2015), un-

derlining the challenges that arise, such as the provi-

sion of an intuitive tool capable of supporting deci-

sion-makers at an early stage in the planning process

or the need for tools that can perform parametric anal-

yses at neighborhood level, taking into account eco-

nomic and environmental parameters. This is sup-

ported by (Meskel & Weber, 2017), while reviewing

seven European cities and their tools for energy and

urban planning, finding a lack of adequate instru-

ments for energy planning at urban scale as well as

110

Köhler, S., Sihombing, R., Duminil, E., Coors, V. and Schröter, B.

A Multi-Scale, Web-based Application for Strategic Assessment of PV Potentials in City Quarters.

DOI: 10.5220/0010406201100117

In Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2021), pages 110-117

ISBN: 978-989-758-512-8

the need for improvement of diagnosis tools to sup-

port early-stage decision-making. In addition,

(Mavromatidis et al., 2019) concludes that any energy

modeling and simulation application on city-quarter

scale should be user-friendly, meet the industry´s

needs and be available either commercially or as open

source. Lastly, (Schoof et al., 2013) shows that tools

that assess for example solar potentials based on Ge-

ographic Information Systems (GIS) allow to im-

prove interactions between key stakeholders, such as

communal planners and energy producers.

Using 3D building stock models allows to process

exact building volumes, surfaces, roof shapes etc.,

whereas 2D data can lead to inaccurate information in

particular regarding PV potentials, if roof geometries

or shadowing effects cannot be assessed properly.

Well-developed tools for energy system analyses

like TRNSYS (Thermal Energy System Specialists,

LLC, 2020) or EnergyPlus (U.S. Department of En-

ergy’s Building Technologies Office, 1996-2019) in

combination with the graphical interface of OpenStu-

dio (Brackney et al., 2018) simulate energy demands,

peak loads and consider a wide range of technologies.

These tools feature user-friendly interfaces and oper-

ate on 3D building information. However, since they

need detailed inputs for each building, they are not

applicable to city quarters, in particular in early plan-

ning stages. In contrast, urban information platforms

such as Solarpotenzial 3D-Stadtvermessung Wien

use large scale 3D data as input and focus on the sim-

ulation of solar potentials, but does not take other

technologies into account. (Stadt Wien, 2018)

(Alhamwi et al., 2019) gives an overview of cur-

rent GIS-based urban energy system models, like

City-Sim, DUE-S and others, introducing the plat-

form FlexiGIS. FlexiGIS uses 2D GIS data as input

and the open-source toolbox urbs, which is an extend-

able tool for the investigation of energy scenarios at

urban scale, considering PV, solar, wind, biomass and

hydropower (Alhamwi et al., 2018). As a case study

the city Oldenburg, Germany was assessed, but not

on a single-building basis (Alhamwi et al., 2019).

Re3ason is another platform that analyzes energy

demands, renewable energy as well as technology po-

tentials (wind, photovoltaics, biomass), and adds a

techno-economical optimization for the energy sys-

tem on top. However, the 2D spatial resolution is re-

stricted to municipal boundaries. (Mainzer, 2019)

This work introduces a web-based tool that on the

one hand handles the complexity of calculating en-

ergy demands, evaluating and dimensioning possible

renewable energy supply systems scenarios with a

solid understanding of the associated potentials at a

detailed, sub-city level. On the other hand, it provides

a clear, user-friendly 3D visualization, which enables

the assessment of a quantitative and technology-neu-

tral verification of the technical and financial feasibil-

ity as well as the efficiency of neighborhood strate-

gies and local energy concepts on a granular level in

real time. The work is presented in five chapters. Fol-

lowing the introduction, the methodology is ex-

plained in Chapter 2. Chapter 3 summarizes the re-

sults and gives a case study demonstration, followed

by a discussion in Chapter 4 and a conclusion in

Chapter 5.

2 METHODOLOGY

The proposed tool enhances the already established

energy simulation platform SimStadt. SimStadt uses

the open data model City Geography Mark-up Lan-

guage (CityGML) (Coors et al., 2016), i.e. 3D build-

ing models, as principal source of input. SimStadt has

a range of databases and calculation routines, e.g.,

photovoltaic rooftop potentials (Nouvel et al., 2017),

building heating/cooling (Eicker et al., 2018) or water

demands (Bao et al., 2020b) implemented and vali-

dated. It utilizes the dynamic energy simulation en-

gine INSEL (Schumacher, 2020) and is structured

along modular workflows that allow its users to eval-

uate different energy technologies, making it easier to

compare different technologies and create combined

scenarios. The simulation within SimStadt can be per-

formed on user-defined areas, as long as a CityGML

file is available, enabling the simulation of both a few

individual houses and entire cities. Provided its base

of 3D city models is geometrically correct, SimStadt

can assess building energy and water demands, refur-

bishment measures, and the integration of renewable

energy systems (PV, solar thermal, biomass) with

high accuracy, and offers the option of 2D visualiza-

tion and results in csv-format (Bao et al., 2020a;

Braun et al., 2018). SimStadt has been successfully

applied to inner-city quarters (Dochev et al., 2020),

quarters dominated by single-family houses (Weiler

et al., 2019) with hundreds of buildings as well as to

larger ensembles such as Brooklyn, a district of New

York (Eicker et al., 2020), featuring thousands of

buildings.

The present work enhances the existing tool by

adding a method for assessing key financial metrics

to the preexisting workflow calculating rooftop PV

potentials and establishes a web based user-friendly

GUI.

A Multi-Scale, Web-based Application for Strategic Assessment of PV Potentials in City Quarters

111

2.1 3D User Interface

In the context of smart cities, geovisualization and

visual analysis are applied to better understand under-

lying data and identify trends, patterns and contexts,

making a city's economy, mobility, environment,

people and management smarter (Harbola & Coors,

2018). Compared to 2D, a 3D geovisualization offers

a more realistic (over-)view and can include detailed

features, such as building specifications and physical

representations, that provide better understanding of

the urban environment (Esri, 2014). With regards to

3D urban visualization options, the Web Graphics Li-

brary (WebGL) (Khronos Group, 2020) is a cross-

platform web standard for rendering interactive 2D

and 3D graphics in a compatible web browser without

requiring plug-ins (Evans et al., 2014).

A study comparing X3DOM (ICG, 2020), three.js

(Three.Js, 2020) and CesiumJS (CesiumJS

contributors, 2020) as an open-source WebGL frame-

work in web-based geospatial applications is pre-

sented in (Krämer & Gutbell, 2015). The study re-

veals that it is possible to develop a geospatial appli-

cation using three.js or X3DOM, even though, unlike

CesiumJS, these two frameworks do not explicitly

support the geospatial reference system. In conclu-

sion, the study reveals that the investigated frame-

works were developed from different approaches and

goals, e.g., aiming at geospatial or non-geospatial ap-

plications, and that the selection of the right frame-

work depends on the use case.

While the intention of CityGML is per se not to

visualize 3D buildings in a web-browser, it can be

converted into 3D Tiles format, applying CesiumGS

(CesiumGS contributors, 2020), without losing sub-

stantial properties, such as building or surface IDs.

3D Tiles is an open specification for streaming mas-

sive heterogeneous 3D geospatial datasets across

desktop, web, and mobile applications.

The realization of a browser-based visualization

in the front-end, based on CesiumJS and 3D Tiles, al-

lows users to interact with SimStadt, INSEL and other

data sources running in the back-end, without in-

stalling new software. For technical and financial as-

sessments, users can submit their input data and pa-

rameters to SimStadt in the back-end via the web-

based GUI. After calculation, the result is sent back

to the front-end and visualized in 3D in the web-

browser. The visualization is carried out by mapping

the result data to 3D Tiles, which holds the building

and surface IDs from the CityGML file used in the

analysis process. Additionally, the CesiumJS-based

browser can be underpinned with OpenStreetMap sat-

ellite image (OpenStreetMap contributors, 2020).

To evaluate the usability of the new web-based

application, a survey and a structured interview was

conducted with five users. Participant A is the head

of climate department of a mid-sized German city,

while participants B to E are computer sciences grad-

uate students at University of Applied Sciences

Stuttgart. While the interview with participant A

aimed at evaluating the usability of the new applica-

tion for strategic energy planning at city level, partic-

ipants B to E were expected to provide more general

feedback on the technical implementation and poten-

tial improvements of the GUI and background pro-

cesses. Participant A did the following tasks under su-

pervision:

 Find the address in an urban area that is sought

in the task.

 Run the newly implemented PV potential and

financial analysis workflow within the web-

based GUI and assess its results (with the de-

fault input values and participant-defined input

values).

After these tasks were finished, the participant

gave feedback on the 3D visualization and the user-

friendliness of the application. The evaluation with

participants B to E aimed at assessing the applica-

tion’s usability for persons with less knowledge of the

energy sector. Without assistance, the participants

were asked to do the following tasks:

 Find a specific house by the address in the tar-

get urban area.

 Run the PV potential and financial analysis

with default input parameters.

While working on the tasks, measurable indicators

were recorded, such as the number of clicks or the time

taken for above-mentioned tasks. Furthermore, the tar-

geting of participants’ clicks or whether participants

encountered problems was assessed and rated from 1

to 5, with 1 being the best and 5 worst score.

2.2 Dynamic Cost Analysis of PV

Rooftop Installations

To assess the financial feasibility of a given technol-

ogy, a flexible economic analysis was established us-

ing the example of the workflow that calculates roof-

top photovoltaic potentials within SimStadt (Eicker et

al., 2018). Based on data from the CityGML file

(building geometries and orientation), the pre-exist-

ing PV workflow calculates physical parameters, rel-

evant for assessing installation cost or LCOE, in par-

ticular the installed power in kW

, annual yield in

kWh/a and specific yield in kWh/(kW

a) on a single-

building level.

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112

Since installation cost are subject to economies of

scale, i.e., specific cost for larger installations are

lower than for smaller ones, all else being equal, dy-

namic cost functions are established. The cost func-

tion is determined by two customizable data points

(S1 and S2), which represent the installation cost in

EUR/kW

for a small system (S1), e.g., 10kW

, and a

larger rooftop PV system (S2), e.g., 100kW

. A loga-

rithmic fit function through S1 and S2 establishes the

dynamic cost function (1) as:

= A - B* log(P

/ 1kW

) (1)

With:

= installation cost for a PV system of a given

size [EUR/kW

]

A = installation cost for a PV system with 1kW

[EUR/kW

]

B = cost digression factor [EUR/kW

]

= nominal power [kWp]

Figure 1 shows a graph of a potential cost func-

tion, with S1 defined as (10kW

| 1,300EUR/kW

)

and S2 as (100kW

| 1,000EUR/kW

), based on

(Fraunhofer ISE, 2020). In that case, specific installa-

tion cost decrease by -23% from 10kW

to 100kW

Figure 1: Dynamic cost function (1), with data points

(10kWp | 1,300EUR/kWp) and (100kWp |

1,000EUR/kWp).

Further parameters that influence the economic at-

tractiveness of a PV system, namely cost of capital as

well as operating and maintenance cost as a percent-

age of installation cost, are also considered. To not

only calculate system LCOE but to rate the financial

attractiveness of a given system, information on local

electricity prices and potential feed-in tariffs needs to

be considered as well.

In all cases, users can either keep default values or

customize parameters in the web-based GUI, allow-

ing to conduct parameter studies and the creation of

simple scenarios in real-time. Table 1 shows the pa-

rameters and their default value setting.

Table 1: Default financial parameters for PV rooftop system

assessment, 1: (Fraunhofer ISE, 2020) p.24,71,8; 2: (Statis-

tisches Bundesamt [Destatis], 2020) p.48; 3: (Bundesnet-

zagentur, 2020); 4: (KPMG International, 2020).

Parameter Value Unit

Asset life time

20 year

Self-consumption rate

30 %

Operating cost as share

of installation cost

1.0 %

Electricity cost

(Germany)

30.0 EURct per kWh

Feed-in tariff

8 EURct per kWh

Cost of capital

2 %

Results of the financial assessment of rooftop PV

systems include total investment cost in EUR, operat-

ing and maintenance costs in EUR/a, LCOE in

EURct/kWh, net present value (NPV) in EUR, inter-

nal rate of return (IRR) in %, the asset’s (discounted)

payback period in years as well as a statement on fi-

nancial feasibility (yes/no) for each roof, assumed

“yes” if the payback period is less than 20 years.

3 RESULTS

3.1 Visualization and Usability

The web-based user interface is split in two main

parts, a menu window on the left, and a 3D visualiza-

tion window on the right part of the screen. Figure 2

displays the GUI after starting the application and

highlights four options a user can use to adjust the

visualization window: No.1 defines the menu where

the user choses the geographical location and technol-

ogy to be assessed, and where parameters are custom-

ized. Furthermore, result graphs are shown therein.

With No. 2, the user can set time, date and the rate at

which time advances for visualizing shadowing ef-

fects, which are important for understanding inner

city rooftop PV potentials. In the top right corner, No.

3 offers the option to search for an address, help nav-

igating the map or running simulations. Lastly, No. 4

is an information window indicating specific building

information, such as building ID and the year of con-

struction, when moving the cursor over a particular

building.

A Multi-Scale, Web-based Application for Strategic Assessment of PV Potentials in City Quarters

113

Figure 2: Screenshot of the web-based user interface, with

four call-outs. 1: main menu, 2: time and date setter, 3:

search and help options, 4: information pop-up for particu-

lar buildings.

When conducting the usability study with the cli-

mate protection manager (participant A) the given

tasks were handled effortlessly. The feedback con-

tained remarks such that the 3D map is intuitive, the

3D visualization as well as the summarized results in

pie chart format are comprehensible, and the custom-

izable parameters are beneficial for creating scenar-

ios. Suggestions for further improvement included the

need for pop-up information buttons explaining input

parameters. Additionally, participant A asked for a

more detailed coloring of the results and the possibil-

ity for a multidimensional result graphics e.g., plot-

ting the total nominal power in kW

of a whole urban

area under a particular total investment in EUR value.

Participants B to E solved the given tasks in on

average about three minutes. The majority of this time

was related to the processing time of the application,

i.e. the data flows to and from SimStadt as well as the

simulation’s calculation time. Of the four partici-

pants, one (D) was not able to find the target address

and the survey was terminated after 4m15s. All re-

sults from this survey are summarized in Table 2.

In general, the participants found the 3D map to

be intuitive. As potentials improvements, a more de-

tailed interpretation of the analysis results was sug-

gested, particularly when looking at the results of an

individual building as opposed to a city quarter. Also,

the menu options were considered as too complex.

Based on that feedback, this feature was revised. As

described in chapter 2, an expert user now choses two

values for installation costs for PV rooftop systems of

two sizes, as well as other parameters. This can either

be done by moving slide bars or setting the parameter

value in specified text boxes. The slide bars with pre-

defined ranges were established in order give the us-

ers orientation about realistic price ranges, e.g. in

terms of installation cost per kW

. Additionally, fur-

ther tooltips and user instructions have been imple-

mented, in particular instructional videos that explain

the most relevant steps and features.

3.2 Case Study Demonstration

To demonstrate the potential of the enhanced tool and

GUI, a part of the inner-city quarter of Stöckach in

central Stuttgart was assessed in detail. The CityGML

file of Stöckach contains 187 buildings, of which 106

are residential. The year of construction of the build-

ings varies from 1934 to 2015. Furthermore, the total

roof area is 29,901m², of which 25,151m² are as-

sessed as in principle suitable for PV systems, as ar-

eas under 20m² are not considered for reasons of prac-

ticability. It is assumed that 30% of the area of flat

roofs and 40% of the area of tilted roofs can effec-

tively be covered with PV modules, taking the eleva-

tion and shading of the modules as well as roof edges

and further rooftop installations, e.g. HVAC systems,

into account. (Bergner et al., 2018).

A minimum insolation of 950kWh/(m² a) is set as

threshold for installing financially feasible PV sys-

tems in Germany today (Fraunhofer ISE, 2020), ef-

fectively excluding north-facing areas or shadowed

roofs. Taken these restrictions into account, Sim-

Stadt’s rooftop PV workflow operating in the back-

end calculates the available effective roof area for PV

with 8,974m², a total nominal power potential of

1,155kW

with a potential yield of 1,187MWh/a.

Table 2: Results of usability study with participants B to E. Row 1 to 3 are measurable indicators, row 4 to 7 are observation

indicators. For line 4 and 5, a rating was given based on participants performance, with 1 being best and 5 being worst.

Participant B Participant C Participant D Participant E

1. No. of clicks 7 20 26 5

2. Handling time 3 Min 23 Sec 2 Min 5 Sec 4 Min 15 Sec 2 Min 35 Sec

3. Finished task successfully? Yes Yes No Yes

4. Have first clicks been target orientated? 1 1,5 1 1.5

5. Did participant run into problems? 2.5 2.0 3.5 1.5

6. Most helpful feature Intuitive map - Intuitive map -

7. Feature that can be improved Expert mode Expert mode Search for address -

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114

After running the analysis, a dropdown menu al-

lows the user to choose which parameters is visual-

ized, color coding all buildings of the chosen area.

Moreover, pie charts evaluate the parameter of choice

on an aggregated level. Parameters that can be chosen

are: potential yield, specific yield, LCOE, total in-

vestment, discounted payback period, and financial

feasibility.

Figure 3 summarizes some output options of the

web-based application the case study of Stöckach.

Photovoltaic yields in MWh/a per roof are colored in

dark red to light yellow shades (top left). The specific

yield in kWh/(m²a), in the top right corner, helps to

identify roofs that are particularly attractive. Dark

colors indicate higher (specific) yields. The bottom

left corner shows the total investment per roof (dark

blue shades represent higher investments), while the

bottom right section marks the discounted payback

period in green. The darker the shade the smaller the

value for the discounted payback period.

Figure 3: Visualization of PV potentials and economics;

Top left: PV yield in MWh/a; Top right: LCOE in

EURct/kWh; Bottom left: total investment in EUR; Bottom

right: discounted payback period in years.

By clicking on individual roofs, detailed results

are displayed in a pop-up table in the upper right cor-

ner, providing an individual evaluation. All results

can be downloaded as *.csv-files or in *.png/ jpeg-

format. In addition to the 3D visualization, all result

values can be exported in text or table format.

Table 3 shows an excerpt of the information that

can be exported, with a focus on the newly added fi-

nancial parameters.

Key results for the case study Stöckach include:

 Total investments per roof range from

1,550EUR to 42,890EUR

 LCOE ranges from 6.4 EURct/kWh to 13.5

EURct/kWh, comparable with (Fraunhofer

ISE, 2020), where the LCOE ranges from 8 to

14EURct/kWh

 Discounted payback periods range from 7.1

years to 17.9 years

4 DISCUSSION

A server-oriented software architecture for urban

simulations, based on standard interfaces (simulation

as a service) and generally available data, with data

integration via a 3D city model in a web browser, cre-

ates a tool that can reach a wide range of users. By

enhancing the SimStadt energy simulation platform

through linking it with a web-based interface and add-

ing an economic analysis to a pilot workflow, such a

tool was established. The tool´s usability has been

tested with energy experts and non-experts, and their

feedback was implemented. There is awareness that

number of participants for the usability study was too

very small to draw final conclusions, nonetheless the

feedback was very valuable for the development of

prototype layout of the GUI. More comprehensive us-

ability studies with the revised tool are planned.

The tool in its current state allows users to calcu-

late detailed photovoltaic potential in real-time; with

this information, installation schedules can be devised

that prioritize buildings based on amortization peri-

ods, or advertising campaigns that target neighbor-

hoods with high PV yields first.

The simulation of the case study illustrates under

which framework conditions the installation of inner-

city PV systems is profitable. Since this kind of sim-

ulation depends on many factors such as local

weather, costs or a given regulatory circumstances, it

is important to be able to run multiple scenarios in

real-time. In this respect, the presented tool provides

the user with these options with a low entry barrier

thanks to its browser-based GUI.

Table 3: Excerpt from the PV potential simulation and feasibility calculation of Stöckach for a selection of roof surfaces.

Building

Area Irradiance Nominal

power

Yield PV specific

yield

Total

investment

LCOE Net

present

value

Internal

rate of

return

Financial

feasibility

[-] [m²] [W/m²] [kW

] [MWh/a] [kWh/ (kW

a)] [EUR] [EURct/ kWh] [EUR] [%] [-]

0006b63 103.7 140.8 6 6.29 1,049 9,132 10.33 4,394 6.48 yes

0038d34 990.7 145.0 44 47.50 1,080 46,028 6.89 59,849 12.80 yes

001b05b 32.0 109.6 1 0.82 816 1,950 17.00 -320 0.21 no

A Multi-Scale, Web-based Application for Strategic Assessment of PV Potentials in City Quarters

115

5 CONCLUSION

The presented tool offers municipalities, urban plan-

ners, project developers or utilities the possibility to

model costs and potentials of a renewable energy

technology for areas comprising a few buildings up to

an entire city, without sacrificing calculation accu-

racy. The browser-based architecture and GUI render

the application accessible and intuitive, requiring no

prior installation of software.

Applying the tool to a case study showed that the

technical and financial results were consistent with

other recent studies, both for the entire quarter as well

as at individual building level. The fact that partici-

pant A in his function as climate protection manager

applies the current version of the tool frequently to

discuss potential PV locations with local businesses

and the city council gives (anecdotal) evidence of its

usefulness.

The advantage of the presented approach resides

in the scalability of the application, which utilizes

typically available 3D CityGML models as a founda-

tion, which means that (i) spatial resolutions from sin-

gle house perspective to whole cities are possible and

(ii) further workflows, e.g. on building heating and

cooling demands or refurbishment potentials, may be

added with reasonable effort.

Since the methods presented here are generic, they

will be transferred to other energy technologies that

are already implemented in the desktop version of

SimStadt, but also to new workflows, e.g., on socio-

economic parameters such as income levels are rates

of house ownership on district level. Such a tool can

be an innovative, integral instrument enabling a more

holistic planning of energy concepts at regional, city

or neighborhood level early on in the decision-mak-

ing process, as it integrates technical potentials, cost

parameters and other decisive factors, such as rates of

house ownership in a district, which is a relevant fac-

tor in decision making, e.g. with regards to building

renovation or PV installations. Given its technology

and manufacturer independent approach, such a tool

would also create the necessary levels of transparency

and trust in its results for decision makers to act upon.

ACKNOWLEDGEMENTS

The financial support provided by the Federal Minis-

try of Education and Research (BMBF) under the pro-

motion and supervised by the project executing or-

ganization VDI Technologiezentrum GmbH for the

project i_city is gratefully acknowledged. Further-

more, we like to thank Alexandra Mittelstädt and

Chris Kesnar, which contributed also to the project.

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