Decision support systems for energy efficiency in...
Transcript of Decision support systems for energy efficiency in...
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
1
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.
2
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.
3
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
4
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
5
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,
6
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.
7
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
8
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:
9
“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
10
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).
12
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.
14
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.
15
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)
16
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.
17
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
18
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
19
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
References
Abastante, F., Lami, I., & Lombardi, P. (2017). An Integrated Participative Spatial Decision
Support System for Smart Energy Urban Scenarios: A Financial and Economic Approach.
Buildings, 7(4), 103. doi:10.3390/buildings7040103
Adkins, T., Kiss, B. and Ness, B. (2011). Overcoming barriers to energy efficiency retrofit
measures in Swedish homes: An actor and policy analysis. Master Thesis.
Alanne, K., 2004. Selection of renovation actions using multi-criteria “knapsack” model.
Automation in Construction, 13(3), pp.377-391.
Antonucci D., Pasut W. (2019). D3.1 Key Performance Indicators (KPIs) and needed data.,
pp.4-31.
Asmelash H.B., Ananta S.C, Sikder S. (2015). Overcoming financial barriers for making
transition happen: A proposal to retrofit privately owned buildings for energy efficiency in
Frankfurt am Main
Buffat, R., Schmid, L., Heeren, N., Froemelt, A., Raubal, M., & Hellweg, S. (2017). GIS-
based Decision Support System for Building Retrofit. Energy Procedia, 122, 403-408.
doi:10.1016/j.egypro.2017.07.433
Capozzoli, A., Corno, F., Corrado, V., & Gorrino, A. (2015). The Overall Architecture of a
Decision Support System for Public Buildings. Energy Procedia, 78, 2196-2201.
doi:10.1016/j.egypro.2015.11.318
C. Chen, M. Cho and C. Lee, "Design and Implementation of Building Energy Management
System," 2016 3rd International Conference on Green Technology and Sustainable
Development (GTSD), Kaohsiung, 2016, pp. 106-111.
Chen, Y., Hong, T., & Piette, M. A. (2017). Automatic generation and simulation of urban
building energy models based on city datasets for city-scale building retrofit analysis. Applied
Energy, 205, 323-335. doi:10.1016/j.apenergy.2017.07.128
IEA, 2019. International Energy Agency. [Online]
Available at: https://www.iea.org/topics/energyefficiency/buildings/
[Accessed 23 March 2019].
Guardigli, L., Bragadin, M. A., Fornace, F. D., Mazzoli, C., & Prati, D. (2018). Energy
retrofit alternatives and cost-optimal analysis for large public housing stocks. Energy and
Buildings, 166, 48-59. doi:10.1016/j.enbuild.2018.02.003
Gökçe, H. U., & Gökçe, K. U. (2014). Multi dimensional energy monitoring, analysis and
optimization system for energy efficient building operations. Sustainable Cities and Society,
10, 161-173. doi:10.1016/j.scs.2013.08.004
Hussain, S., Gabbar, H.A., Musharavati, F. and Pokharel, S., 2013, August. Key performance
indicators (KPIs) for evaluation of energy conservation in buildings. In 2013 IEEE
International Conference on Smart Energy Grid Engineering (SEGE) (pp. 1-6). IEEE.
24
Hong, T., Chen, Y., Lee, H.S., & Piette, M.A.(2016). CityBES: A Web-based Platform to
Support City-Scale Building Energy Efficiency. Conference: 5th International Urban
Computing Workshop.
Johansson, T., Vesterlund, M., Olofsson, T., & Dahl, J. (2016). Energy performance
certificates and 3-dimensional city models as a means to reach national targets – A case study
of the city of Kiruna. Energy Conversion and Management, 116, 42-57.
doi:10.1016/j.enconman.2016.02.057
Johansson, T., Olofsson, T., & Mangold, M. (2017). Development of an energy atlas for
renovation of the multifamily building stock in Sweden. Applied Energy, 203, 723-736.
doi:10.1016/j.apenergy.2017.06.027
Kim, S.A., Shin, D., Choe, Y., Seibert, T. and Walz, S.P., 2012. 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. Automation in Construction, 22, pp.51-59.
Kolokotsa, D., Diakaki, C., Grigoroudis, E., Stavrakakis, G., & Kalaitzakis, K. (2009).
Decision support methodologies on the energy efficiency and energy management in
buildings. Advances in Building Energy Research, 3(1), 121-146. doi:10.3763/aber.2009.0305
Kumbaroğlu, G. and Madlener, R., 2012. Evaluation of economically optimal retrofit
investment options for energy savings in buildings. Energy and Buildings, 49, pp.327-334.
Lucon O., D. Ürge-Vorsatz, A. Zain Ahmed, H. Akbari, P. Bertoldi, L. F. Cabeza, N. Eyre, A.
Gadgil, L. D. D. Harvey, Y. Jiang, E. Liphoto, S. Mirasgedis, S. Murakami, J. Parikh, C.
Pyke, and M. V. Vilariño, (2014): Buildings. In: Climate Change 2014: Mitiga-tion of
Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona,
E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B.
Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)].
Cambridge University Press, Cambridge, United Kingdom and New York, NY, US
Madrazo L., Sicilia Á., Massetti M., Plazas F.L, Ortet E. (2019, June). Integrating and
processing building energy data to support decision making.
Malatji, E. M., Zhang, J., & Xia, X. (2013). A multiple objective optimisation model for
building energy efficiency investment decision. Energy and Buildings, 61, 81-87.
doi:10.1016/j.enbuild.2013.01.042
Moghadam, S. T., Toniolo, J., Mutani, G., & Lombardi, P. (2018). A GIS-statistical approach
for assessing built environment energy use at urban scale. Sustainable Cities and Society, 37,
70-84. doi:10.1016/j.scs.2017.10.002
Murugesan, L.K., Hoda, R. and Salcic, Z., 2015. Design criteria for visualization of energy
consumption: A systematic literature review. Sustainable Cities and Society, 18, pp.1-12.
Murshed, S., Al-Hyari, A., Wendel, J., & Ansart, L. (2018). Design and Implementation of a
4D Web Application for Analytical Visualization of Smart City Applications. ISPRS
International Journal of Geo-Information, 7(7), 276. doi:10.3390/ijgi7070276
25
Nižetić, I., Fertalj, K., & Milašinović, B. (2007, January). An overview of decision support
system concepts. In 18th International Conference on Information and Intelligent
Systems (pp. 251-256).
Oh, T.K., Lee, D., Park, M., Cha, G. and Park, S., 2018. Three-Dimensional Visualization
Solution to Building-Energy Diagnosis for Energy Feedback. Energies, 11(7), p.1736.
Ouhajjou, N., Loibl, W., Fenz, S., & Tjoa, A. M. (2015). Stakeholder-oriented Energy
Planning Support in Cities. Energy Procedia, 78, 1841-1846.
doi:10.1016/j.egypro.2015.11.327
Petersen, K., Feldt, R., Mujtaba, S. and Mattsson, M., 2008, June. Systematic mapping studies
in software engineering. In 12th International Conference on Evaluation and Assessment in
Software Engineering (EASE) 12 (pp. 1-10).
Skarbal, B., Peters-Anders, J., Malik, A. F., & Agugiaro, G. (2017). How To Pinpoint
Energy-Inefficient Buildings? An Approach Based On The 3D City Model Of Vienna. ISPRS
Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-4/W3, 71-
78. doi:10.5194/isprs-annals-iv-4-w3-71-2017
Solmaz, A. S., Halicioglu, F. H., & Gunhan, S. (2016). An approach for making optimal
decisions in building energy efficiency retrofit projects. Indoor and Built Environment, 27(3),
348-368. doi:10.1177/1420326x16674764
Staso, U. D., Giovannini, L., Berti, M., Prandi, F., Cipriano, P., & Amicis, R. D. (2015).
Large-Scale Residential Energy Maps: Estimation, Validation and Visualization Project
SUNSHINE - Smart Urban Services for Higher Energy Efficiency. Communications in
Computer and Information Science Data Management Technologies and Applications, 28-44.
doi:10.1007/978-3-319-25936-9_3
Takim, R. and Akintoye, A., 2002, September. Performance indicators for successful
construction project performance. In 18th Annual ARCOM Conference (Vol. 2, pp. 545-555)
UN, 2019. Sustainable Development Goal. [Online]
Available at: https://sustainabledevelopment.un.org/sdg7 [Accessed 20 March 2019].
UN, 2019. Sustainable Development Goal. [Online]
Available at: https://www.un.org/sustainabledevelopment/climate-change/ [Accessed 20
March 2019].
Yeo, I., & Yee, J. (2016). Development of an automated modeler of environment and energy
geographic information (E-GIS) for ecofriendly city planning. Automation in Construction,
71, 398-413. doi:10.1016/j.autcon.2016.08.009
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.