IN DEGREE PROJECT TECHNOLOGY,FIRST CYCLE, 15 CREDITS

, STOCKHOLM SWEDEN 2020

Decision support systems for energy efficiency in buildingsa review of existing models and its potentials

THEJAN RANGANATHAN

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT

TRITA TRITA-ABE-MBT-2028

www.kth.se

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Abstract

Energy conservation and decarbonization of the building stock is a way to achieve sustainable

development goals. Visualizing and monitoring energy consumption with a help of Decision

Support Systems (DSS) can help to inform and support making decisions to conserve energy,

reduce emissions, save costs and improve overall quality of life. However, there are no clear

guidelines to how such tools should be designed, and which demands from the different

stakeholders they should meet. This literature review presents an overview of existing DSSs

that calculate, optimize, visualize and monitor energy usage in buildings. A total of 22 studies

have been selected through an in-depth literature search and analysed in a study matrix split

into four categories describing relevant features that are vital for each DSS. The study has

identified that main functions of analysed DSSs are: 1) to compare costs for CO2 emission

reduction or energy saving for various actions; and 2) to compare current energy performance

of buildings. Finally, it has shown a variety of needs for different stakeholders that affect the

choice of methods and data used by DSS. Hence it is crucial to ensure early alignment of the

needs and functions for the developed tools, in order to be efficient in decision-support for

better energy efficiency and climate mitigation.

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Foreword

This report is a Degree Project in Energy and Environment that has been written in the last

semester of third year in the Energy and Environment programme at KTH. I would like to thank

my supervisor Oleksii Pasichnyi for supporting me throughout the whole semester enabling me

work on this report independently. I would like to thank him further for initiating ideas and

clear visions on the project outcome and continuously delivering improvements for the content

and structure of the report.

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Contents

ABSTRACT 1

FOREWORD 2

1. INTRODUCTION 5

1.1 Aim 6

2. BACKGROUND 7

2.1 Building retrofitting 7

Key Performance Indicators (KPI) 8

Benchmark 8

2.2 What is a Decision Support System? 8

2.3 Urban data and information systems for decision making 9

3. METHODS 11

3.1 Literature search 11

3.2 Scope of analysis 11

3.3 Content analysis 12

4. RESULTS 13

4.1 Initial literature search 13

4.2 Papers selected for analysis 13

4.3 Findings from the content analysis 15

I. Optimization methods for retrofit options 15

II. GUI-based energy monitoring 17

III. Visualization, monitoring and benchmark of building stock 17

IV. Interactive 4D canvas 19

4.4 Stakeholder Analysis 20

5. DISCUSSION & CONCLUSIONS 21

REFERENCES 23

APPENDIX A. STUDY MATRIX (SELECTED STUDIES ANALYSED BY THE

RELEVANT FEATURES) 26

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Nomenclature

DSS Decision support systems

GHG Greenhouse gas

GIS Geographical Information System

GUI Graphical User Interface

HVAC Heating Ventilation Air Conditioning

KPI Key Performance Indicator

ICT Information and communication technologies

IEA International Energy Agency

MCDA Multiple- Criteria Decision Analysis

MOP Multi-objective programming

SDG Sustainable Development Goals

UN United Nations

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1. Introduction

The adoption of the United Nations Sustainable Development Goals (SDGs) in 2015 marked a

new level of political acknowledgement of the importance of energy to development (IEA,

2019). One of its goals is to “ensure access to affordable, reliable, sustainable and modern

energy for all” which is also known as the SDG 7 (UN,2019). SDG 7 aims for access to reliable

and modern energy services through improvement of energy efficiency (UN,2019). With rising

greenhouse gas GHG emissions, the effects of climate change are observed worldwide (UN,

2019).. Therefore, another goal - SDG 13 calls to” take urgent action to combat climate change

and its impact” (UN,2019). It particularly targets (with SDG Target 13.2.1) that each state

improves their ability to adapt to climate change impacts through integration of appropriate

measures in the respective national policies/strategies/plans (UN,2019). The Inter-Agency and

Expert Group on SDG Indicators developed the global indicator framework with accordance to

the 2030 Agenda (UN, 2019).

Cities are one of the most energy consumers in the world where 60% of total building final

energy use are accounted from urban buildings (Moghadam et al.,2017). For the primary energy

use, the building stock accounts for 30- 40 % and corresponding carbon footprint in many

countries. Most CO2 emissions are also emitted in the cities (Ouhajjou et al., 2016). The

building sector contributes to 40 % of indirect and direct CO2 emissions and buildings

consumption levels land on 36 % globally (IEA,2019). This energy use may double or

eventually even triple by mid-century because of several key factors. Some of the factors are

population growth, migration to cities, the change of household sizes and the change of

lifestyles and wealth on a global scale. However, the final energy use might decrease if modern

cost-effective techniques are used (Lucon et al., 2014).

Building retrofitting is a key strategy to improve energy efficiency in existing building.

Furthermore, it is a way to mitigate climate change, reduce GHG emissions, minimize

environmental impacts, improve people’s quality life and generate economic benefits. By

improving building energy efficiency there are opportunities to create and transform cities into

a more sustainable environment. The understanding of physical characteristics, energy use and

operating patterns is required to plan and evaluate a retrofit strategy in buildings. Different

stakeholders face challenges for having limited and disparate data and tools (Hong et al., 2016).

It is a challenge to motivate the end-users to conserve energy which could be primarily based

upon the lack of awareness and knowledge for their energy consumption (Murugesan et al.,

2015). To motivate the end-users to rationalize the energy usage it is important to provide

software applications that provide energy visualization and decision support for possible action

user choices. However, there’s a certain challenge for such software to evolve further due to

the multitude of needs for different stakeholders and related uncertainty with the end-use cases

to be addressed. Even though there is a continuous interest for this area to develop there is still

a need for clarification on to how to design an effective visualization (Murugesan et al., 2015).

This literature review focuses on common Decision support system (DSS) tools that are used

for better energy use of building stocks in cities and in general urban areas. Furthermore, this

review focuses on common DSS features for analysing and finding optimal retrofit solutions,

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energy benchmarking and energy monitoring by different stakeholders. This review act as a

basis to show the research trend in this area.

1.1 Aim

The aim of this study is to explore and demonstrate the current research knowledge on how

decision-making related to the building energy performance in cities can be supported with the

provision of the relevant information to different stakeholders through the web-based services

or any other platforms.

The following objectives will be addressed to reach the aim with the help of a literature analysis:

1. To explore the state-of-the-art of urban decision-support for improving energy

performance of buildings in cities.

2. To analyse what are the typical purpose, functions, methods and usage scenarios of

decision-support systems (DSSs) for building energy efficiency.

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2. Background

The purpose of this section is to provide the readers a background for the literature review

process conducted further. Base knowledges about energy and retrofitting in buildings is

provided for the reader to grasp the idea of practical aspects of energy consumption

improvements. Furthermore, the background section will explain the fundamental ideas of a

Decision Support System (DSS) concept and current problems that exists regarding DSSs for

energy efficiency. The diversity of stakeholders and their demands and understanding of

different data and methods of the DSSs creates a difficulty in creating efficient tools for energy

analysis.

2.1 Building retrofitting

The world’s total energy consumption from buildings are 40% and investing in energy efficient

retrofit projects would be beneficial. Inefficient facilities are substituted by better advanced

energy efficient facilities which depends on the size of the funding (Malatji et al., 2013). This

is the concept of retrofit which is generally used to identify alternatives that are “required to

bring building into a framework of new requirements” (Alanne K., 2003). The challenge to

reduce energy consumption in the building sector is to find effective strategies for retrofitting

existing buildings (Kumbaroglu et al., 2012). Recent technology advances offer the need of

retrofit solutions to improve energy efficiency in buildings. The building envelope consists of

roof, external walls, windows, doors and floors and improving its thermal property is one of the

most economical way to reduce energy needs under ongoing operating conditions (Kumbaroglu

et al, 2012). Choosing from several economical optimal set of retrofit measures requires a

detailed technical evaluation of the building envelope, evaluation of energy systems for

supplying heating and cooling and external and internal indoor climate properties, so that the

retrofit alternatives are analysed and their energy-saving potentials are calculated accurately

(Kumbaroglu et al, 2012). The energy behaviour of a building is affected mostly of how the

selection of building materials and components combined with a building envelope and

different systems of HVAC (Heating Ventilation and Air Condition) and lighting (Hussain et

al., 2014).

From Malatji (2013), there are various examples of some possible solutions that for enhancing

energy performance in buildings which are the following:

Improving buildings by insulating the roof and substituting single glazing windows to

double ones and install solar shading

Improving lighting system by replacing to LED or CFL lightning

Improving HVAC systems with advanced controls

Substituting inefficient equipment such as cathode ray tube computer monitors with

liquid crystal displays

To improve power factor by installing power factor correcting capacitors (Malatji et

al., 2013)

These are just a few improvements that could be made through different retrofit techniques.

One of the main problems with these are funds where the property owner needs to decide

whether it is worth to invest due to unclear benefits (Malatji et al., 2013). Despite the rising

energy prices, the ability to combat climate change and the benefits from a retrofit option there

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are still uncertainties whether to perform a retrofit to an existing building or not (Adkins T,

2011). The problem is that there are three different kinds of barriers that creates this uncertainty.

Knowledge barriers around how retrofit could be beneficial is the first problem. Energy use is

not visible, and the consequences are not obvious therefore not understanding how much costs

could be saved with improved energy conservation. Uses of life-cycle costs or other instruments

for decision-making is rare in the building sector which indicates that the true costs of measure

and energy use is unknown. Involved stakeholders have different incentives and motives which

doesn’t align together for the sake of improving energy performance (Adkins T, 2011). There

are certain financial barriers that are considered where, for instance, private homeowners don’t

have the capital fund for retrofit projects. This in turn could indicate that the full potential of

energy performance couldn’t be achieved with a lesser version of a retrofit due to lack of fund.

Also, improved energy performance doesn’t reflect the property value which discourages the

owners. (Asmelash et al., 2015).

Key Performance Indicators (KPI)

In order to understand if a retrofit is fulfilling the requirements and the needs of the investors

and owners, certain Key Performance Indicators (KPIs) are implemented. In general, “(KPIs)

reflect project’s goals and provide means for the measurement and management of the progress

towards those goals for further learning and improvement.” (Antonucci et al., 2019). KPI are

defined in the form to guide the design development, comparing design solutions and support

decision making. The purpose of KPIs are to measure the performance of buildings and to

provide useful information (Antonucci et al., 2019). There are a variety of KPIs to measure, for

instance, economic performance and environmental and reliability performances. Economical

KPIs include such as operational and maintenance costs for energy resources in a building.

Environmental KPIs include GHG emissions, materials, water, safety, waste and environmental

risk factors (Hussain et al., 2014).

Benchmark

Another way to measure if a retrofit is fulfilling the requirements is to create benchmarks for

the project. From Takim (2002), the definition of benchmark is “as a systematic process of

comparing and measuring the performance of the companies (business activities) against others,

and using lessons learned from the best to make targeted improvements.” (Takim et al., 2002).

There are two reasons for benchmarking one, is for companies to gauge where they stand

against competitors and two, they want to learn and use successful ideas from the best

companies (Takim et al., 2002). For instance, in Gökce (2014), they use the “CIBSE Guide F

Benchmarks” in order to achieve optimal energy performance in buildings. The created

Decision Support System (DSS) from this study is integrated with this benchmark which acts

as the standard to be achieved for.

2.2 What is a Decision Support System?

A decision support system (DSS) is widely used and covers different types of information

systems aimed to support human decision making. Some definitions provided are:

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“Decision support systems (DSS) are tools to assist decision makers in complex

decision-making processes” (Buffat et al., 2017)

“DSS is a computer-based system that aids the process of decision” (Nizetic et al., 2007)

“DSS is an interactive, flexible and adaptable computer-based information system,

especially developed for supporting the solution of a non-structured management

problem for improved decision making” (Nizetic et al., 2007)

The above definitions implement the variety of system modelling of answering complex human

decision making. The DSSs are specifically designed to only alleviate decision processes and

only support rather than generate decision making. Furthermore, the different DSS are designed

to adapt to the needs of a decision maker. There are five different kinds of DSS which are

document-driven, communication-driven, data-driven, model-driven and knowledge-driven

decision support systems (Nizetic et al., 2007). This project focuses on communication-driven

and model-driven DSS. Communication-driven DSS is based upon the idea of using network

and communication technology to enable communication and collaboration with different users

which ensures a faster and a productive decision making. A model driven DSS consists of

analytical and optimization tools to suggest possible actions (Nizetic et al., 2007).

2.3 Urban data and information systems for decision making

One of the greatest challenges of this century, with regards to climate change and the need to

develop sustainable use of energy, is urbanization. To address these issues, a combination of

data generation of cities and new energy simulation tools are needed to explore opportunities

for urban energy models (Hong et al.,2016). The availability of relevant technologies in the

current phase has encouraged the development of many research projects in this area with

publicly available data to create an energy map (Staso et al., 2015). In recent years, sensor

systems, software services and data standards have been developed due to the immense increase

of the appearance of smart city information and communication technology (ICT). Much of the

smart city related research lies in the field of environment, energy and sustainability which

requires explorative analysis and visualization of multidimensional 2D, 3D and 4D data.

(Murshed et.al, 2018).

To evaluate the current energy use in cities there needs to be ways to compare, contrast, rank

and estimate strategies. It is necessary for cities to evaluate building retrofit opportunities for

their building stocks with regards to energy usage, size, vintage, type, ownership and

socioeconomic potential. Cities’ authorities need quantitative decision analysis tools that bring

together measured data, physics and data-driven models. To design and operate such systems

require active computer simulation and optimization which comprises the different types of

building systems, weather data, user behaviour and operating patterns (Hong et al., 2016).

Other demands and needs are needed to be fulfilled such as modelling of solar energy or PV

potential where the results are needed to be aggregated at different temporal (hourly, daily,

monthly, annual) resolutions. (Murshed et.al, 2018).

In recent years, the importance of energy data has increased where many different platforms

take advantage of these. Static data of building stocks has been relied on these systems. Systems

such as Sunroof, uses vast amount of geo-referenced data from Google to evaluate the potential

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of PV on roofs (Madrazo et al., 2019). Increasing amount of data is now available in open

format where many different energy information systems have been created for these data to be

available for different stakeholders. The problem is that these data is only displayed rather than

integrated in multiple sources to get specific results from specific analysis. Furthermore, the

problem is the variety of different data that limits the analytical capabilities of different energy

information systems (Madrazo et al., 2019). For instance, a commonly used data model to store

and model building objects in Smart Cities application is the 3D city model. E.g., one of the

standards for 3D city models is the data format of CityGML (Murshed et.al, 2018).

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out on any relevant paper, the remaining search engine (Web of Science Core Collection) were

also used to acquire more papers. However, these two databases don’t have the ability to export

the document results into an Excel file with the required details and therefore these were

collected separately. One additional study will be included to the study matrix that is not

processed through a search engine which was provided by the supervisor.

3.3 Content analysis

The further content analysis was conducted through the systematic mapping (Petersen et al.,

2008) of following DSS features:

Purpose of the study

Input Data

Methods

Function (What does the DSS allow for end-users)

End-users

User Interface (Nizetic et al., 2007)

Each study was first analysed to identify its purpose, summarizing what problems exists and

why a DSS is needed for decision making. The second features involve the methods which

describes which process is being done to make the DSS work and how to get the relevant value

for the end-users. The input data consists of any data that are usually collected by different

institutes which are relevant for the purpose of creating the platform. Relevant data may consist

of building data or geodata. The building data consists of heat energy consumption or energy

data from different building components. Building components are the wall or windows which

give different values of for instance density, conductivity or cost. The function describes the

possible outcomes of the used platform to the end-users depending on which alternatives they

make and what type of data that are available. The end-users of the platform are part of the

decision-making process which could consist of building managers, city planners, public

persons, real estate owners, architects, and engineers. Different platforms are created with

different types of data for suitable end-users. The important part of using any DSS is the user

interface (UI) or interface where a user-friendly and a thought through UI is needed for a

simplified usage. Two parts form a logical UI where the first part lets the user define the

specified request. The specified request is entered through text or depending on what available

options there are. The second part consists of the built system to return the results of the

searched request. The results of the request are represented by text or graph (Nizetic et al.,

2007). This project has several kinds of user-interfaces that are used practically or by visual

images. Visual images can consist of 2D, 3D and even in 4D that are used by different users.

These features will be summarized into a matrix from each relevant study which will consist of

four different categories that refers to different types of DSS. The purpose of this matrix is to

systematize and understand which decision-support tools are suitable for each user and how

these tools can contribute towards communication and decision making. The purpose of this

matrix is also for the readers to get an overview of all the relevant studies which is summarized,

and this will act as a base for the results part. Furthermore, it is easier to detect any DSS feature

that are frequently appearing. Another table is also created for a stakeholder analysis in order

to list out which relevant stakeholders can use respective DSS tool.

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After applying the inclusion and exclusion criteria (Table 1), 21 studies are ready to be included

in the study matrix. Adding with the paper received from the supervisor, in total, 22 studies will

be included in the study matrix where the studies will be numbered from 1 to 22 (Appendix A).

The number of studies that are obtained for every search query with respective search engines

are illustrated in Table 2.

Table 2. Number of accessible papers from the initial search results from Scopus search engine and the

number of papers selected for further analysis.

Search queries Search engine Initial number

of papers

Number of

papers after

applied criteria

Urban AND Decision support

systems AND energy efficiency

Scopus 52 2

Decision support systems AND

Energy efficiency AND retrofit

Scopus 42 2

Energy monitoring AND

buildings AND cities

Scopus 22 4

Web based tools AND energy

monitoring AND Buildings

Scopus 0 0

Urban AND Web application

AND energy AND Visualization

Scopus 5 1

Web based tools AND energy use

AND buildings

Scopus 5 2

Decision support systems AND

Energy Efficiency AND

Buildings AND Cities

Web of Science

Core Collection

64 10

The highest number of studies that are relevant for this study is obtained through the search

query of “Decision support systems AND Energy Efficiency AND Buildings AND Cities”

which was 10 from the search engine of Web of Science Core Collection. The total number of

relevant studies that are received from Scopus search engine was nine. The overall process for

choosing the relevant paper is followed by Figure 1 below.

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Figure 1. Workflow of the literature search

4.3 Findings from the content analysis

The results of analysis of the selected papers formalized by the defined features is provided in

Appendix A. This section further provides the summary of identified studies along the four

main categories they were distributed into:

I. Optimization methods for retrofit options (4 papers)

II. GUI-based energy monitoring (3 papers)

III. Visualization, monitoring and benchmark of building stock (14 papers)

IV. Interactive 4D canvas (1 paper)

I. Optimization methods for retrofit options

The results from category I from Appendix A shows that there are four different studies that are

using different optimizing and simulation methods to reach an optimal retrofit solution. The

common functions between the four studies is to compare the saved energy with costs. Another

common feature between all the studies in this category is the access of building data and cost

data or cost function.

Database Research

Scopus

Web of Science

Core Collection

Selection Process

Exclusion Criteria

Inclusion Criteria

Identify Relevant Literature

Relevant Title

Relevant Abstract

Relevant features

Purpose

Methods

Input Data

Function

End-users

Interface Study Matrix

(provided in

Appendix A)

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Methods Three studies [1-3]1, used several optimization methods to reach an optimal energy retrofit

solution while in [4], specific cost optimal approach (from the directive 2010/31/EU EPBD

recast) was performed. The goal of the cost optimal approach is to establish a comparative

methodology framework for the calculation of cost optimal levels of minimal energy

performance requirements for buildings (Guardigli et al., 2018). In the case of [2], the model

formulated as a multi-objective optimization problem where NPV (net present value), initial

investment, energy target and payback period are used as constraints. These factors are solved

using generic algorithms (Gas) (Malatji et al., 2013). From [1], two methods are used namely

MOP approaches and MCDA approaches for both offline and online approaches. During the

decision support process, the decision maker must take into consideration of several aspects

such as environmental, energy, financial and social to make an optimum design (Kolokotsa et

al., 2009).

MCDA

Multi-criteria decision analysis (MCDA) is widely used to help decision makers make decisions

in an organized way. There are a variety of different uses of MCDA but in general, “a finite or

infinite set of actions (alternatives, solutions, and options), some decision criteria, and at least

one DM.” (Abastante et al., 2017). The popularity of MCDA increased amongst urban planning

decisions since this method can account qualitative and quantitative aspects (which consists of

environmental, social and economic aspects) (Abastante et al., 2017). In [1], MCDA is used in

the operational and retrofit stages. Combinatorial and outranking methods are used to analyze

indoor air quality, energy consumption and thermal comfort (Kolokotsa et al., 2009). One

aspect of MCDA is the ability to assigning hierarchical importance to each criterion which

impacts the decision that they make. This assists the user in choosing the preferred solution

when undesired solutions increase during the optimization stage. In [3], a multi-criteria rating

method is used to rank the order of retrofit solutions alternative with regards to heating and

cooling energy consumption, indoor air quality and cost (Solmaz et al., 2016).

Interface for optimization methods For studies [1-4], the DSS targeted different end-users with having two different interfaces. To

analyse the outcome of the tool, 2D visualization is appropriate from these. 2D visualization is

a traditional way of representing quantitative data to enhance understanding. Some common

2D visualization representations are graphs and charts. The chart visualization can be split into

basic and advanced chart visualization. Bar chart, line chart, pie chart, box chart is some of the

names that classifies as basic chart visualization while advanced chart visualization consists of

geo chart (Google Maps), time log, cluster maps, time chart etc. (Murugesan et al., 2015). In

our case, the 2D visualization consists of tree diagram from [1], bar chart from [2], scatter and

line plots from [3,4]. In [2], the optimization method was used in several cases listed from case

A to case F where the results consist of payback period, energy savings and NPV. Together

with a sensitivity analysis these results are then listed as a bar chart to compare the six cases.

In [3], three different scenarios are analysed with the GenOpt optimization method and the

1 Hereafter [X] is used to refer to the analysed studies provided in the Appendix A by the order of their

appearance.

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results were displayed as a scatter plot. The line and scatter plots consist of payback period and

energy savings where the user can select any data on the plot. In [4], different graphs are used

for different units. Bar charts are used for 17nalysing the NPV for thirteen different retrofit

strategies while line and scatterplots are used for 17nalysing energy data.

II. GUI-based energy monitoring

Three studies [5,6,7] were selected to fit under the category II. from Appendix A. As stressed

by (Gökce et al., 2013), “The importance of analyzing, monitoring and optimizing building

energy consumption is vital for renovation and energy-efficient operations of buildings where

it allows to identify inefficient energy consuming buildings”. The particular methods and

respective tools can vary but in general various data integration techniques are used. In [5], a

form of ETL (Extract, transform, load) technique is used for data integration with the help of

java script while in [11,14], ETL is used for visualizing energy consumption. ETL is a technique

to extract large information data sets from different sources, transform collected data and finally

to load different results for the purpose of analysis or visualization (Johansson et al., 2017). In

the case of visualization of energy map, the ETL tool can generate city energy models in 3D on

various levels (Johansson et al., 2016). In [6] and [7], data integration techniques are done with

BEMS (Building Energy Management Systems) with [6] data is collected and monitored

regularly from Taiwan Power Company (C.Chen et al., 2016). In [7], the BEMS feature is

integrated into their own created DSS called OPTIMUS DSS.

Interface GUI (Graphical User Interface) has the purpose of representing building information

performance to the stakeholders with regards to their background and roles. In [6], several

interviews were conducted with structural questionnaires were carried out for several industrial

partners. The purpose of this was to create the a friendly-user GUI for defined stakeholders

which in this case were for facility manager, occupant, building owner and building technician

(Gökce et al., 2013). The GUI created in [7] and [8] monitors real-time update energy

consumption with the help of BEMS.

III. Visualization, monitoring and benchmark of building stock

In total, there are 14 studies that were found that matches under category III. in the study matrix.

The purposes of all the tools from III were to find optimal retrofit options with energy maps

while some studies had the purpose of only visualizing, monitoring and benchmarking energy

usage in specified building blocks. There are several similar and distinct features for every DSS

studied.

Various data integration techniques for visualization Various data integration techniques are used for collecting and merging data. For many of the

studies [4,8,9,10,13,18], according to the study matrix, EnergyPlus is used.

EnergyPlus EnergyPlus is described as “an open-source whole building energy simulation program that

models both energy consumption (for HVAC, lighting, and plug and process loads) and water

use in buildings” (Chen et al., 2017). Building-energy simulation are created to evaluate the

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performance of selected building systems. Quantitative information is not provided, regards to

energy efficiency with building improvements, without any building- energy simulation (Oh et

al., 2018). Energy retrofit analysis are based upon the system of EnergyPlus for commercial

buildings. Nonprofessional’s will have difficulties using the simulation program and large

information is required to calculate with high accuracy. (Oh et al., 2018). EnergyPlus is built

upon other tools such as CityBES (Chen et al., 2017) where it shows from the study matrix that

CityBES is an integration tool used for analysing energy usage in buildings. Two studies [9,18]

from Appendix A have used CityBES as their web-based platform.

CityBES “CityBES is a web-based platform to simulate energy performance of a city’s building stock,

from a small group of buildings in an urban district to all buildings in a city” (Hong et al., 2016).

The tool is built upon the LBNL Commercial Building Energy Saver Toolkit, where the purpose

of the LBNL is to provide retrofit analysis of commercial buildings of medium and small retails

and offices. CityBES will account other commercial building types such as hotels and hospitals

as well as residential buildings for single and multi-family members. Data such as district

heating and cooling, ECMs (Energy Conversion Measures) for new commercial and residential

building types are all handled by a parallel computing architecture to take advantage of the of

the high-performance computing(HPC) clusters (Hong et al., 2016). From [9], the CityBES is

used for UBEM (Urban Building Energy Modeling) to support city-scale building energy

efficiency analysis. The tool provides a 3D visualization with GIS which includes a color

coding for energy use intensity (EUI) (Chen et al., 2017). In [18], CityGML and GIS is used as

a data schema for the representation of urban building stocks in the tool. This provides a 3D

visualization which also shows a colour coding for the resulted energy simulations (Hong et al.,

2016).

In the study matrix, CityGML is used in [9,16,18,19] indicating the frequency of the input data.

CityGML is used to store urban data models which is the core layer for CityBES. It enables the

possibility to store data from various sources and provide inputs to the modeling, analytics and

GIS visualization. With CityGML as an input data, it is possible to visualize buildings, bridges,

city furniture, transportation, land use, transportation etc. (Hong et al., 2016). Publicly available

data generally don’t include all the information needed for an energy performance simulation

which requires an estimation of data in a more reliable way. CityGML is used for this and

estimates the building energy performance (Staso et al., 2015).

Stakeholders and city managers use CityBES to evaluate options for energy use reduction by

quantifying and prioritizing building retrofit solutions. The tool has the capacity of modeling

10000 or more buildings and identify 30 to 50 % energy savings. It is also possible to enable

research to explore the options of using energy storage or simultaneous heating and cooling. In

[18], CityBES has been used by different stakeholders where it implements a suite of analytics,

simulation and visualization functions. The different stakeholders that performs energy analysis

with CityBES are urban planners, city energy managers, energy consultants and researcher for

city projects. The tool is used for four different cases. It is used for energy benchmarking with

the help of Energy Star Portfolio Manager (rating system) and Building Performance Database

(BDP) data inputs. The tool can provide optimal strategies for energy systems, for city policy

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makers to make decisions regarding retrofit and to improve city building stock operations

(Hong et al., 2016).

GIS Many tools are developed with an integration of computation models together with GIS to

acquire input data for thousands of buildings where designers and urban planners have full

access to visualize results (Hong et al., 2016). GIS is frequently used in energy-related

renovation plans where it provides the capability to visualize, analyse and plan the energy use

in buildings on both regional and local scales (Johansson et al., 2017). It has been frequently

used to support the visualization and energy use in urban districts. In Johansson (2017), 3D GIS

Model is used to analyse complex patterns of energy use in urban districts with the help of

building stocks. Furthermore, visualizing energy consumption helps to monitor energy use,

analysing and predicting energy use and provision of real time feedback. (Johansson et al.,

2017). For the different stakeholders of energy advisors, real estate companies and urban

planners, a city energy model is a valuable tool for them. With an integration of EPC data, it is

easier to follow a guideline for these stakeholders to benchmark the energy use in building

stocks (Johansson et al., 2016).

Interface for visualization “3D Visualization is more realistic and psychologically appealing for the human brain”

(Murugesan et al., 2015). Classification of 3D is also split into traditional and modern

visualization. Traditional visualization consists of chart visualization which means that they

could be drawn using a spreadsheet application while modern visualization includes 3D

mapping and user interface. In many different domains, 3D visualization has been of an

increasing interest for use due to its advantages over 2D. For cases such as urban modelling and

to analyse and visualize building objects, 3D becomes necessary to use. (Murugesan et al.,

2015). From categories III and IV, various interfaces are used to display for the different

stakeholders. A combination of 2D and 3D visualization techniques are merged for the end-

users to analyse the results.

For the interfaces from categories III and IV, all the tools [8-21] have an interactive map for

visualizing respective building blocks for respective functions. The clients used are different.

For instance, in [21] ENERPLAN and ENERVAL was created from scratch were the purpose

of ENERVAL was to inform owners about the energy performance of the buildings, identifying

energy measures and its costs. The purpose of ENERVAL was created for experts such as urban

planners, policy makers and politicians to understand building stock energy performance.

Furthermore, studies such as [8-10,13,15,17,20] uses their own clients to display 3D models for

visualization. While other studies such as [11,12,14,17] used Google Earth client to be able to

display the building stocks in chosen areas. Finally, tools [16,18] used WebGL client to display

their 3D models.

IV. Interactive 4D canvas

Only one study was found under the category of IV. from Appendix A. The method used for

this system is spatial analysis and energy simulations. There is a vast amount of input data

needed for this system to work and the function of this system is to compare energy performance

20

in different building stocks. These data are supported by the dynamic visualization interface

which consists of JSON, GeoJSON, CityGML, Cesium Markup Language(CZML) and Cesium

3D Tiles. Like previous studies, the use of CityGML as a data storage and modelling is

common. It is also used for energy simulations with specific data formats used. The interface

that is proposed in [22] is used for both personal computers, which consists of desktops or

laptops and in multi-touch tablets. The 4D Canvas is intended to visualize energy simulation

results of 3D geospatial and time-dynamic data but also it can analyse the results according to

their own will. The developed navigation system is intended for analysing the results with the

help of their own developed GUI for representing multiple energy model outputs. This system

is also unique compared to the other ones as it has multi-touch screen function.

4.4 Stakeholder Analysis

All identified end-users were classified into the four categories defined in this study (Table 3).

However, some of the studies does not explicitly mention (or mentions the end-users in a

vaguely way) any specific stakeholder for their respective Decision Support Systems (DSSs)

and therefore difficult to list in Table 3. Furthermore, one study from category IV. from the

study matrix doesn’t mention any end-user either and therefore is excluded from Table 3. Seen

from the table, majority of the Urban and Urban Energy planners uses the tools from category

III. which also applies for city authorities. The only stakeholders that are using DSS tools under

category I. and II. are Designers and constructors, building manager and building owners. Nine

different tools could be used by city authorities while build owners can use tools from any of

the categories.

Table 3. End-users attributed for different categories of DSSs

Category (I, II, III) /

End-users

Urb

an

pla

nn

ers

Bu

ild

ing

man

ager

s

Bu

ild

ing o

wn

ers

En

ergy a

dvis

ors

En

ergy

Man

ager

s

Cit

y A

uth

orit

ies

Desi

gn

ers

an

d

Con

stru

ctors

Resi

den

ts

I. Optimization methods 1 1 2

II. Monitoring with GUI 1 1 1 1 1

III. Visualization of energy 8 1 4 3 2 8 3

Total 8 3 6 3 2 9 3 4

21

5. Discussion & conclusions

The conducted literature review has resulted into a variety of DSS features. The detailed

literature analysis was done with the purpose of not losing any relevant studies that showcases

the current research trend in Decision support systems (DSSs) for energy efficiency. However,

there were some issues when filling in knowledge about the 22 tools in the study matrix. The

difficulty of pinpointing exact information in each feature made it difficult to analyse the result

part. For instance, from study [19] it was difficult to specify which energy simulation that was

used for the publication not providing specific information.

This sector has been identified to have the potential of saving energy (Johansson et al., 2017).

In Europe, most buildings consist of building stocks that have low energy performance. A need

for a rapid transition towards low-carbon scenario is necessary in order to achieve national

priorities which consists of strategic goals in regional and local levels (Moghadam et al.,2017).

Furthermore, more efficient buildings can generate economic benefits and improve people’s

quality of life (Hong et al., 2016). As seen from the 22 studies, the purposes of these tools are

to increase energy performance of buildings with regards to economic and environmental

aspects through different options gained from the respective tools. They all share similar

purposes where DSSs are the perfect way to simulate and forecast how each decision will

decrease the amount of energy consumption and saving costs.

The study could have improved by adding some perspective from software creators and the

relevant stakeholders. The purpose of this is to create some criteria’s for how their ideal

Decision Support System (DSS) should be created and implemented regarding energy

performance. This could have done by an interview or through finding perspective of different

stakeholders from literature search. Other alternatives could have been to perform a survey

analysis to the different stakeholders around the city of Stockholm and to gain perspective of

their current uses of different DSS. From the different perspective, different DSS features could

have been chosen to study and compared in the study matrix. A larger section of background

should have been created and more information about building retrofit should have been

provided to increase the understanding of technical aspects of building energy performance.

However, this report is intended for certain readers and therefore the complexity of this study

is made to be as simple as possible. Creating the perfect DSS is still difficult due to the different

demands and the available technology and data and therefore the complexity of this will be

further researched through future studies and recommendations.

This area of research has a vast amount of potential in reaching optimal energy performance

together with digitalisation. The increasing development of mobile phones, PCs, internet,

embedded displays and a variety of other systems enables optimal management of energy.

Therefore, future studies of energy performance. In addition to the increase development of

different technology is also the availability of different data from different data sources

(Madrazo et al, 2019). The potential of merging the sources into one platform is an important

aspect of working towards sustainable development reaching several goals. There is still a need

for future work regarding different efficient statistical and datamining technique to increase

understanding from such complex information to the different stakeholders. This in turn

increase the likelihood of improved decision making (Murugesan et al., 2015). Studies

22

regarding energy consumption together with social networking is an important aspect for future

work (Murugesan et al., 2015).

The conclusion of this study shows that a variety of needs from the different stakeholders affects

the choice of different methods and data used by a DSS which is crucial to ensure early

alignment of the needs and functions for the developed tool. The main functions of the different

DSS analysed are to compare investment costs with CO2 savings or energy performance

improvement with regards to different retrofit options and comparing energy performances of

different building stocks. The main methods and tools that were used for the respective DSS to

work are MCDA, EnergyPlus, CityBES and GIS. The main data that are common from the

different publications are spatial data from CityGML and GIS databases, different kinds of

building and energy data (e.g. thermal-physical properties of the building envelope,

measurements of energy use). The integration of these features is vital to reach the targets of

sustainable development. Although, energy performance improvements are often made for

economic reasons, it is also a direct link towards decarbonization and energy conservation

which in turn leads to combating climate change and improved access to energy.

23

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26

Appendix A. Study matrix (selected studies analysed by the relevant features)

Author(s) and title

of the study

Purpose of the

development of

DSS

Methods Input Data Function End-Users Interface

I. Using optimization methods to find optimal retrofit solutions

1. (Kolokotsa et al.,

2009)

Decision support

methodologies on the

energy efficiency and

energy management in

buildings

To analyse the decision support methods used for energy efficiency

and environmental quality enhancement in buildings.

Multi-objective programming optimization techniques (MOP)

Multi-criteria decision

analysis techniques (MCDA)

TRNSYS

Energy Plus

Visual DOE

Building data from existing and new buildings.

Cost Function

To compare strategies for retrofit with consideration of environment,

energy, financial and social aspects.

Designers, architects, building scientists.

Displayed Strategy and Tree Diagram

2. (Malatji et al., 2013)

A multiple objective

optimization model for

building energy

efficiency investment

decision

To assist the

decision-makers for optimum retrofit action to fulfil the investment criteria which is to save energy and to minimize payback time.

Multi-objective optimization

problem with a constraint set which includes NPV, initial investment, energy target and payback period which is solved by a genetic algorithm.

Building data from 25 inefficient

facilities

Cost Function

Highest energy

savings and low payback time.

Only mentions

designers and decision makers that have the responsibility of retrofit actions.

Bar chart consisting

of Energy Savings and Payback Period.

3. (Solmaz et al., 2016)

An approach for

making optimal

decisions in building

energy

efficiency retrofit

projects

To decide the best energy efficiency retrofit option in existing buildings and this approach

was used in a school building in Izmir, Turkey.

Building Energy Model (Sketch-Up Open Studio)

Sensitivity analysis (SimLab, MatLab, EnergyPlus)

Optimization(GenOpt)

MCDM(Multi-criteria decision making)

MOO(Multi-objective optimization)

Building data from school building

Energy Utility Data

Cost Data

Comparing energy savings and payback period for each retrofit option.

Design and construction professionals, building experts and investors

Visualizing energy data with scatter and line plots.

27

4. (Guardigli et al.,

2018)

Energy retrofit

alternatives and cost-

optimal analysis for

large public housing

stocks

To assess different

renovation strategies that have a relationship between economical sustainability with achieved energy efficiency.

NPV

PBP

GC(Formula from Study)

BQE(Building Quality Evaluator)

Building Data

Cost Data

Comparing energy

usage with retrofit cost.

Building owners but

also states decision makers.

Visualizing data with

scatter and line plots.

Bar chart for NPV

II. Energy monitoring and benchmarking in buildings with GUI

5. (Gökce et al., 2013)

Multi-dimensional

energy monitoring,

analysis and

optimization for energy

efficient building

operations

To propose a methodology to reduce energy consumption in buildings.

Java script with ETL(Extract, Transform, Load) tools application

BIM

CAD Design Tool

Energy Consumption Data

Building Data

CIBSE Guide F Benchmarks

Comparing:

Zone Temperature,

CO2 emissions,

electricity consumption

ventilation data.

Friendly user

Created four different displays for:

Facility Manager

Occupant,

Building owner

Building technician

GUI energy monitoring

Real-time visualization of energy consumption

6. (C.Chen et al., 2016)

Design and

Implementation of

Building Energy

Management Systems

To propose an energy management system for energy and electricity

savings in Center Building.

Collecting data with server monitoring

Data integration

BEMS(Energy Management

Systems)

Energy Data

Building Data

Comparing electricity usage

Displaying energy demand

Does not explicitly mention any users

GUI with real-time update

BEMS(Building Energy Management

Systems)

7. (Capozzoli et al.,

2015)

The overall architecture

of a Decision Support

System for public

buildings

To find solutions to minimize the CO2

emissions and energy consumption

Energy Simulation

Data mining techniques and inferencing rules

Building static data(building and technical systems) and building dynamic data

Static data consists of:

- Heating system - Occupancy - Space heating capacity

Dynamic data consists of: - Weather forecasting - Sensor based data - Social Media - Energy Prices

- Renewable energy production data

Forecasted outdoor air temperature

Building occupancy

Thermal comfort

Forecasted energy prices

City authorities but does not state any general end-user

BEMS(Building Energy Management Systems)

Displaying Graphs as

results

28

III. Visualization of energy performance of building stocks with an interactive map with monitoring and benchmarking functions

8. (Buffat et al., 2017)

GIS based decision

support system for

building retrofit

To propose a web based DSS using GIS based building stock model

EnergyPlus

TRNYS

GIS

GIS

Spatial Data

Building Data

Comparing CO2 and energy savings after selecting a retrofit action

Advanced users Web Client

Interactive Map

Bar charts

9. (Chen et al., 2017)

Automatic generation

and simulation of urban

building energy models

based on city datasets

for city-scale building

retrofit analysis

To present retrofit

analysis using CityBES and Energyplus simulation tools based on user

selected ECMs and cities building dataset.

City BES(City building

energy saver)

Energy Plus

OpenStudio

Weather Data

GIS Dataset Building Stock

Database(building technologies, utility data)

CityGML

ECM(Energy Conservation Measures)

GeoJSON

Comparing different

retrofit actions for energy savings and payback period

Urban Planners Interactive map with

3D Models

Different charts for end results

10. (Oh et al., 2018)

Three-Dimensional

Visualization Solution

to Building- Energy

Diagnosis for Energy

Feedback

To present a 3D

visualization solution to aid building managers about energy efficiency recommendations

EnergyPlus (DOE-2, BLAST,

COMIS)

Weather data

Usage profile

Building information

Heating & cooling system

Lightning system

New regenerable energy

Comparing annual

primary energy requirement

Building owners

Building Manager

City Administrators

Residents

Interactive map

Different charts and diagrams

Web and Cloud Client

11. (Johansson et al.,

2017)

Development of an

energy atlas for

renovation of the

multifamily building

stock in Sweden

Visualize the energy

use and the renovation needs in buildings in the largest cities of Stockholm with regards to socio-economic

challenges.

Spatial ETL (Extract,

transform, load technology)

GIS

SCB

Boverket (EPC)

SABO

Lantmäteriet

Comparing energy

performance(kwh/m2) in different building stocks.

City planners

Energy Advisors

Facility managers

Visualization with

Google Earth Map

Tabular Data

29

12. (Kim et al., 2011)

Integrated energy

monitoring and

visualization system for

smart green city

development.

Designing a spatial

information integrated

energy monitoring

model in the context of

massive data

management on a web-

based platform

To monitor and

visualize aggregated and real time states of various energy usages in buildings

Google Earth/Maps

EnerISS

Solution(Simulation) which includes Solver, EMS, Evaluator

EnerGIS

EMS(Energy Management System)

Environmental GIS

Energy Consumption Data

Sensor Data from buildings

Urban Spatial Information

Solver/Evaluator

SCADA

BIM

Google Maps Components

Comparing

consumption measurements of different energy data

Energy suppliers

Energy Managers

Policy makers

Citizens

3D Urban

Environment in Google Earth

Web Based Platform

13. (Moghadam et

al.,2017)

A GIS-statistical

approach for assessing

built environment

energy use at

urban scale

To illustrate a

geospatial bottom-up statistical model for the estimation of energy consumption in building blocks

Multiple Linear Regression

EnergyPlus

Data Collection and Data Integration

GIS Data

Building Data

(And Energy Data)

Comparing energy

consumption from different buildings stocks

Decision makers in

the urban planning process

Urban planners

Urban Energy

Map(2D and 3D)

Scatter Plot

14. (Johansson et al.,

2016)

Energy performance

certificates and 3-

dimensional city models

as a means to reach

national targets – A case

study of the city of

Kiruna

Visualizes the energy situation in Kiruna which was requested by the energy advisors.

Spatial ETL(Extract, transform, load technology)

EPC data(Boverket)

Lidar data

Also data from Lantmäteriet, SCB and LKAB.

Comparing energy performance (cost/saved kwh) in different building stocks.

Energy advisors

Energy and Real estate companies

Visualizing with Google Maps.

Excel Spreadsheet

Power Map

15. (In-Ae Yeo et al.,

2016)

Development of an

automated modeler of

environment and energy

geographic information

(E-GIS) for ecofriendly

city planning

A model was developed to support a strategic technology

implementation of an environmentally friendly local energy planning.

Spatial analysis

Energy Simulation

Urban Space Database

Mesh GIS Database

E-GIS Database

Evaluation Database (LCC,

LCCO2, Stability of energy supply)

Comparing energy performance in different building stocks

Urban planners 3D modelling of urban space

Interactive Map

Tabular data

16. (Skarbal et al., 2017) HOW TO PINPOINT

ENERGY-

To assess the energy performance

Energy Simulations

Spatial Analysis

Building Data

Google Fusion Database

Comparing energy performance in building stocks

Decision makers in urban planning

3DCityDB-Webmap-Client

30

INEFFICIENT

BUILDINGS? AN

APPROACH BASED

ON THE 3D CITY

MODEL OF VIENNA

of residential buildings

Cesium PostgreSQL

Web Graphics Library

CityGML

before and after refurbishment

17. (Ouhajjou et al.,

2016)

Stakeholder-oriented

energy planning

support in cities

Provide information

for different options of energy strategies on different building stocks.

City Sim(energy simulation)

EnerGIS

SynCity

ArcGIS

SEMERGY

Spatial Data

CSV Files

Relational Database

Spread sheets

Comparing

investment costs for saved energy and CO2

Urban Planners

Building Owners

City Administration

Web Client

Google Map

Linked Data Browser

SPARQL endpoint

18. (Hong et al., 2016)

CityBES: A Web-based

Platform to Support

City-Scale Building

Energy Efficiency

To aid possible

retrofit actions for energy efficiency.

Energy Plus

CityBES(City Building Energy Saver)

OpenStudio

Weather data

GIS

Building Stock

Database (consisting of utility data)

CityGML Database

Energy Star Portfolio Manager

(benchmark)

BPD (Building Performance Database) (benchmark)

Comparing energy

use intensity and electricity

Energy managers

Urban Planners

Building owners

Energy consultants

XML-based data

model

3D city models

Building stock filters

Desktop application

19. (Staso et al., 2015)

Large-Scale Residential

Energy Maps:

Estimation, Validation

and Visualization

Project SUNSHINE -

Smart Urban Services

for Higher Energy

Efficiency

To aid visualization of energy

performance of buildings for relevant stakeholders.

Various Energy Simulations

3D City Database

3D City Database

Thermo-physical data

Climatic data

CityGML

Comparing energy performance of

chosen building stocks

Citizens

Public

administrations

Government agencies

WebGL(3D Virtual Globe Interface)(Web

Client)

20. (Abastante et al.,

2017)

An Integrated

Participative Spatial

Decision Support

To aid urban energy

decisions in real-time processes.

EAA(Energy Attribute

Analysis)

MCDA

Spatial analysis

GIS(Spatial Data)

Cost Data

Building Data

Comparing different

building stocks energy scenarios

Urban planners

Policy makers

Visualization with

Dashboard(SDSS)(Spatial Decision Support System)

31

System for Smart

Energy Urban

Scenarios: A Financial

and Economic

Approach

Bar chart energy refurbishment

scenarios

21. (Madrazo et al.,

2017)

Integrating and

processing building

energy data to support

decision making

To aid decision

making towards improving energy performance in buildings with the help of monitored urban and building energy data

Data integration with a public

web portal called ENERSI

EPC Data

Cadaster

geographical data

census sections

technical building

inspections

catalogues of refurbishment measures

building renovation assessment tools

ISO13790:2008 (Performance Indicators)

ENERVAL:

Comparing energy measures and costs

ENERPLAN: Comparing energy consumption and CO2 emissions

ENERVAL(Buildin

g Owners)

ENERPLAN(Policy makers, urban planners, politicians)

ENERVAL

ENERPLAN

IV. 4D Canvas with 3D geospatial data for visualization

22. (Murshed et.al,

2018)

Design and

Implementation of a 4D

Web Application for

Analytical Visualization

of Smart City

Applications

To introduce the 4D Canvas web-based application to

improve decision making in smart cities which consists of 3D geospatial data for visualization.

Data integration with energy simulations and python scripts with the help of different

softwares

Visualization Data: JSON Data for charts representing attributes

GeoJSON Data for building

surfaces

CZML Data for thematic building as a whole

3D Tiles for building surfaces

Energy, Building and Power Data Python Scripts

Time Dynamic Data

Cesium Virtual Globe

Comparing energy performance(W/m2) in different building

stocks.

Cooling demand for buildings.

Hourly based energy consumption.

Users from different sectors

4D Canvas deployed in both desktop and multi-touch screens

with the help of WebGL.

GUI is developed for the integration of 3D spatial and temporal data which represents multiple energy model outputs.