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A Visualization Dashboard and Decision Support Tool for Building Integrated Performance Optimization

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Analyzing the results of multi-objective optimization and building performance simulation can be a very tedious process that requires navigating between different software and tools. There is a clear scarcity in visualization tools that combine methods for big data analysis and design decision support tools that integrate detailed information for each design and parameter. Having a single visualization tool that provides methods to both visualize and analyze a large amount of data, understand the relations between objectives and variables, and having the ability to compare and analyze the preferred designs thoroughly can support the process of design decision-making. In this paper, previous attempts to develop better data visualization tools for both integrated building simulation and optimization outputs were analyzed, then guidelines and a visualization tool prototype that can be effective in decision making and analyzing multi-objective optimizations results were presented.
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A Visualization Dashboard and Decision Support Tool for
Building Integrated Performance Optimization
Mahmoud Gadelhak1, Werner Lang2, Frank Petzold3
1,2,3Technical University of Munich
1,2{m.gadelhak|sekretariat.enpb}@tum.de 3petzold@ai.ar.tum.de
Analyzing the results of multi-objective optimization and building performance
simulation can be a very tedious process that requires navigating between
different software and tools. There is a clear scarcity in visualization tools that
combine methods for big data analysis and design decision support tools that
integrate detailed information for each design and parameter. Having a single
visualization tool that provides methods to both visualize and analyze a large
amount of data, understand the relation between objectives and variables, and
having the ability to compare and analyze the preferred designs thoroughly can
support the process of design decision making. In this paper, previous attempts to
develop better data visualization tools for both integrated building simulation
and optimization outputs were analyzed, then guidelines and a visualization tool
prototype that can be effective in decision making and analyzing multi-objective
optimizations results was presented.
Keywords: Multi-objective optimization, Building Performance Simulation,
Simulation, Visualization tools
BACKGROUND
Due to global climate change and the related risks
and challenges, the need to reduce carbon emissions
is more obvious than ever. Planners, architects, and
engineers are the key players to create a more en-
vironmentally friendly and sustainable built environ-
ment. Building Performance Simulation (BPS) is an
important tool to support the move towards more
energy efficient buildings and is becoming an inte-
gral part of the current design decision-making pro-
cess. BPS software and tools are being rapidly devel-
oped to be more user-friendly and accurate. Despite
the high demand for more user-friendly interfaces,
simulation results’ visualization and analysis tools are
not usually given the deserved attention.
Decision-making and data visualization tools
have a great impact on the final design product. In
a recent survey, nearly 25% of participants (architects
and engineers) identified graphical representation as
their top priority for the user interface of BPS software
(Attia et al., 2009). However, another survey showed
that most users were not satisfied with the Graphi-
cal User Interface (GUI) offered by commercially avail-
able tools. In the survey, 75% of the users pointed out
the lack of a graphic interface for post-processing of
DESIGN TOOLS - THEORY - Volume 1 - eCAADe 35 |719
the BPS optimization results (Attia et al., 2013). As
a result, most of the surveyed users had to depend
on their own post processing skills or self-developed
tools to graphically present the simulation output,
and analyze the data.
The scarcity of efficient data analysis and insuf-
ficient quality of visualization tools is even more ev-
ident in the case of an integrated (holistic) assess-
ment or a multi-objective optimization. In an in-
tegrated design assessment, it can be necessary to
have a dashboard that gives an overview of all the
relevant performance aspects, and summarizing the
performance of the building while simultaneously
provides detailed information where needed. On the
other hand, analyzing a large number of simulation
outputs, such as results from a multi-objective op-
timization, requires more advanced tools to exam-
ine the whole set of data and to find relations be-
tween different objectives and variables. For multi-
objective optimization of integrated building perfor-
mance, there is no single visualization tool that com-
bines methods for big data analysis and design deci-
sion support through detailed information for each
of the relevant aspects. As a result, analyzing the
multi-objective optimization results becomes a very
tedious process and requires navigating between dif-
ferent software and tools. A single tool, that provides
methods for analyzing and visualizing a large amount
of data, clarifying the relations between objectives
and variables, and having the ability to compare and
analyze the preferred designs thoroughly, can sup-
port the decision-making process.
This paper presents guidelines and a preliminary
prototype of a visualization tool that can effectively
support the decision-making in multi-objective op-
timization. In a first step, existing attempts to de-
velop better data visualization tools for both inte-
grated building simulation and optimization outputs
were reviewed and discussed. In a second step, effec-
tive techniques for the creation of a data visualization
tool that can aid in the decision-making process were
presented. And finally, a prototype of a visualization
tool was brought forward as a proof of concept.
PREVIOUS WORK
Analysis of optimization results
Several research works investigated the analysis of
building performance optimization results. Brown-
lee and Wright (2012) sought to analyze the rela-
tionships between design objectives and variables,
using a simple ranking order and correlation coef-
ficient. They used a combination of scatter plots
and spreadsheets to graphically present the opti-
mization results. Scatter plots accompanied by par-
allel coordinates graphs and graphic images were
also used by Chaszar et al., (2016). Such graphs pro-
vided useful feedback, but it was noted that adding
more interactive capabilities could further enhance
the workflow. To help designers better understand
the optimization results, Wortmann (2016) presented
a novel method to represent the results graphically.
His method, called Performance Map, helps in iden-
tifying the optimization problem, relating parame-
ters and performance, examining promising designs,
and guiding automated design exploration. O ther ef-
fective methods were also addressed in other engi-
neering disciplines (Pryke et al., 2007; Witowski et al.,
2009). Nevertheless, while these methods can sim-
plify analyzing a large number of cases, it does not
provide detailed information on each performance
aspect. For instance, while using scatterplots and
parallel coordinates graphs can aid in finding an op-
timal design for daylighting performance, it does
not show how the daylight is distributed within the
space, or at which hours artificial lighting is needed.
Such detailed information and context are necessary
for the decision-making process. The ability to exam-
ine and compare several aspects at the same time is
also equally important.
Integrated performance dashboards
The importance of integrating graphical representa-
tions of diverse performance analysis in a single dash-
board was highlighted by many researchers. The
Daylight-Europe project (DLE) presented the “Inte-
grated Performance View (IPV)”, a multi-parameter
dashboard to compare reference and as-built cases
720 |eCAADe 35 - DESIGN TOOLS - THEORY - Volume 1
(Hensen et al., 1996). This dashboard proved useful in
comparing the overall performance of design cases,
as it integrated different charts and graphs for heat-
ing load, energy consumption, visual comfort, ther-
mal comfort and glare index.
The IPV tool was further developed to provide
more flexibility and customization as well as sev-
eral enhancements for better communication with
users (Prazeres & Clarke, 2005). Struck et al., (2012)
built upon this concept with a special focus on hu-
man cognition and more innovative graphs, such as
temporal maps and motion charts. Other research
works and commercial software also offer an inte-
grated performance dashboard. However, most of
these tools lack the ability to deal with a large amount
of data, and thus cannot be efficiently used to analyze
the results of multi-objective optimizations.
INTERACTIVITY AND VISUALIZATION
TECHNIQUES
Information visualization can be defined as “the use
of interactive visual representations of abstract data
to amplify cognition” (Ware, 2012). Scientific re-
search on information visualization has resulted in
several best practices and guidelines for visualiza-
tion design (Cleveland, 1985; Few, 2006; Tufte, 2001;
Ware, 2012). Interactivity plays a major role in the vi-
sualization tools. Yi et al., (2007) presented seven in-
teractive techniques that can be effectively applied
to the case of building performance optimization.
The first four techniques, Select, Explore, Reconfigure
and Encode, can help the user explore the whole set
of data by switching between different graphical rep-
resentations of data and marking preferred designs.
The other three techniques, Abstract/Elaborate, Fil-
ter, and Connect, can be used to provide detailed in-
Figure 1
A screenshot of the
visualization tool.
A- Context and
design parameters
panel, B- Explorer
panel, and
C-Integrated
dashboard
DESIGN TOOLS - THEORY - Volume 1 - eCAADe 35 |721
formation for selected cases. Adding two other tech-
niques, such as Compare (for directly comparing se-
lected cases) and Advice (as a tool for guiding further
enhancements) allows for quick, yet thorough, com-
parison between preferred cases and supports in-
formed decision-making. These two additional tech-
niques were also suggested by Haeb et al. (2014),
who highlighted the importance of spatial context
and visual feedback as an essential component in the
field of building performance simulation.
VISUALIZATION DASHBOARD: GUIDE-
LINES
Building on the reviewed literature, the following
guidelines, and requirements for a new tool for vi-
sualizing the results of integrated building perfor-
mance optimizations were defined. The suggested
visualization tool can provide better ways to inves-
tigate the building optimization results by offering
three levels of data analysis:
1- Design space overview and exploration
At the first level, the full set of simulation results
should be explored. Multi-dimensional graphs, such
as parallel coordinates and scatter plots, are useful in
this case. Switching between plot types, filtering the
results and selecting favorite cases help in clarifying
basic relations between the objectives and variables,
in addition to highlighting optimal and preferred de-
signs.
2- Sensitivity analysis and parameter rela-
tions
On the second level, the direct relation between any
two variables or objectives can be investigated. The
use of sensitivity analysis and 2D charts can indicate
the variables that drive the optimization process, the
expected enhancement in each objective, and the
relative importance of the design variables.
3- Detailed results and comparison be-
tween favorite designs
At the final level, an integrated dashboard is pre-
sented with detailed performance data, which pro-
vides all the needed information about each selected
design. To ensure an informed decision-making pro-
cess, the visualization tool should offer the ability to
compare the detailed performance and contextual
reference (images and 3D model of the cases) of fa-
vorite cases.
VISUALIZATION DASHBOARD: PROTO-
TYPE
As a proof of concept, a preliminary prototype
was developed according to the above-mentioned
guidelines. The prototype was built using the vi-
sual language programming tool Grasshopper and
HumanUI, a plugin for Grasshopper that enables
the creation of graphical user interfaces. Additional
Grasshopper user objects and code functions were
written to overcome limitations in the HumanUI Plu-
gin. The visualization tool consists of the following
panels.
Context and design parameters. The left panel
shows the names of the selected design alternatives
as well as a zoomable and rotational 3D visualization
and the corresponding design parameters. The user
can change the design parameters to specify they de-
sign alternative (Figure 1-A).
Explore. Alternatively, the user might choose
to select the design from the Explore section. The
Explore section contains a parallel coordinates chart
that shows the design variables and results of the
complete design set with the selected design high-
lighted. Additionally, it also contains a radar or bar
chart for showing the performance of the selected
design as well as a data table. The user can filter
the design alternatives by limiting the values of any
of the variables or the objectives. It is also possible
to mark cases in order to be compared later to each
other (Figure 1-B).
Variable-objective relations. In this panel, the
user can choose a variable(s) and objective(s) to see
722 |eCAADe 35 - DESIGN TOOLS - THEORY - Volume 1
Figure 2
The workflow of the
optimization
process and the
corresponding
visualizations
panels in the
visualization tool.
the direct relation between them, which is rendered
in the shape of a 2D scatterplot chart in the case of a
single variable and single objective, or as a matrix of
scatterplots in the case of several variables and ob-
jectives.
Compare. The compare panel offers a bar chart
to compare the marked design alternatives as well as
simple visualization of each performance objective.
Integrated Dashboard. Similar to the IPV tool
discussed earlier in the literature, the integrated
dashboard houses more details for all the perfor-
mance objectives for the selected design alternative.
Performance objectives tabs. For each perfor-
mance objective, a separate section that includes al-
ternative ways of result visualizations an even greater
detail of result analysis is provided.
To make it easier for the user to explore and
choose preferred designs, the ability to show or hide
DESIGN TOOLS - THEORY - Volume 1 - eCAADe 35 |723
Figure 3
Different design
alternative with
similar energy
savings.
panels was provided. This enables the user to focus
on a specific panel or several ones, e.g., the Explore
& Integrated Dashboard or the Explore & Compare.
Figure 1 shows a screenshot of the visualization tool
prototype with three active panels: Context and de-
sign parameters, Explore and Integrated Dashboard.
CASE STUDY
The visualization tool prototype was used to visualize
the results of a building performance optimization,
in which a parametric model was optimized for the
integrated performance of the following parameters:
Energy consumption, Daylighting, Thermal Comfort,
View and Glare and Renewable Energy. The paramet-
ric model was built using Grasshopper. EnergyPlus
[1] and Radiance simulation engines, used through
the HoneyBee (Roudsari, M. S. & Pak, M., 2013) and
Diva (Jakubiec, J. A., & Reinhart, C. F. 2011) plugins,
were utilized for the energy and daylighting assess-
ments. A multi-objective optimization was carried
out using the optimization tool Octopus (Vierlinger,
R., & Hofmann, A. 2013).
Parametric model
The optimization was carried out for the south façade
of a single office space in Munich, Germany (48°8’N
11°34’E). The office room was assumed had the di-
mensions of 4.00m x 6.50m x 3.00m for the width,
depth, and height, respectively. The parametric
model of the south façade provided different set-
tings for the glazing area, glazing system, shading,
daylighting system, and insulation system. The glaz-
ing area was divided into upper and lower parts,
where the upper part acted as a clerestory window.
Both window parts were introduced to the shad-
ing devices separately. Seven Window-to-Wall Ratios
were studied together with four glazing systems, four
shading systems, and four light-shelf settings. Addi-
tionally, the building insulation was increased gradu-
ally with 2.5 cm steps to a maximum of 25 cm. Over-
all, nearly 20,000 design alternatives can be gener-
ated.
Multi-objective optimization Workflow
A multi-objective optimization was performed with
the evolutionary algorithm SPEA2 using Octopus. A
random generation was created at first, which con-
tained different cases (genomes). Then for each
724 |eCAADe 35 - DESIGN TOOLS - THEORY - Volume 1
Figure 4
Scatterplot matrix
chart showing the
relation between
energy savings,
insulation and
glazing type.
genome, daylighting, energy and thermal comfort
analysis were carried out using DIVA and Honeybee.
At the same time, the openness of the façade to the
view outdoors and the possible area for building inte-
grated photovoltaics was calculated. The results from
the analysis phase were used as the fitness values for
the optimization, namely the spatial daylight auton-
omy, energy savings compared to an unshaded base
DESIGN TOOLS - THEORY - Volume 1 - eCAADe 35 |725
Figure 5
Examples of the
contents of the
performance tabs.
A- Energy use
analysis.
B-Daylighting
performance
analysis. C- Thermal
comfort. D- Visual
comfort (Glare and
View). E- The
compare tab
showing a simple
comparison of three
selected cases.
case, the percentage of comfort hours, view and PV
percentages. The optimization process continued for
10 generations with a population size of 30. The mu-
tation rate was set to 0.5, the mutation probability to
0.1 and the crossover to 0.8. Cases with very high so-
lar exposure were neglected.
During the analysis, the results from the simula-
tions were post-processed into different types of vi-
sualizations according to the results type. After the
optimization ends, the optimization results are also
visualized using parallel coordinates and scatter plot
charts. Figure 2 shows the workflow of the optimiza-
tion process and the corresponding data visualiza-
tion in the prototype.
Optimization results analysis
The optimization resulted in 150 design alternatives.
The results were analyzed in several ways using the
visualization tool prototype. First, the results and
parameters of all the 150 cases were analyzed us-
ing the Explore section. The parallel coordinates
chart offers an interactive tool by which the results
could be filtered for a specific range of values for any
and each of the variables and objectives. Addition-
ally, the results in the data table could be sorted for
any of the variables and objectives. A radar chart
for the objective results is also shown for the se-
lected design alternative. In this case study, the de-
sign alternatives were sorted for highest energy sav-
ings. It was found out that several design alterna-
726 |eCAADe 35 - DESIGN TOOLS - THEORY - Volume 1
tives achieved energy savings between 30-32%. Al-
though these cases achieve a similar energy perfor-
mance, their design parameters and other objective
performances differed greatly. Only one alternative,
for instance, achieved an acceptable daylighting per-
formance value (sDA more than 50%). This enables
the designer to choose his design wisely by taking all
the objectives and also the design features in mind.
A trade-off between the different objectives is of
course necessary. Figure 3 shows the four design al-
ternatives with the highest energy savings and a min-
imum daylighting performance of sDA= 50%. Their
corresponding performance for the other objectives
is illustrated using the radar chart.
In a second step, the relation between variables
and objectives can be studied using scatter plots ma-
trix in a separate window. For instance, the scatter
plot between the energy savings and glazing and in-
sulation shows how triple glazing and double low-
E glazing have a higher potential for energy savings
compare to single and conventional double glazings.
For the insulation, it could be noted that the poten-
tial for energy savings increase with the increase of
the thickness of the insulation. Nevertheless, most of
the cases with 10 cm insulation were able to achieve
energy savings between 25% and 30% (Figure 4).
Tocompare the per formanceof the preferred de-
signs, marked cases are automatically added to the
compare panel, where a simple bar chart comparison
is created. Finally, the integrative dashboard and per-
formance tabs show detailed and alternative visual-
izations for each of the optimized objectives. Other
simulation outputs can also be investigated such as
lighting and occupancy schedules; heating, cooling
and equipment’s load; alternative daylighting perfor-
mance metrics like the daylight autonomy, daylight
availability, ... etc.; glare analysis for various times and
dates; ... etc., Figure 4 shows part of the different tabs
for a single design alternative and the compare tab
with a comparison between the cases with the high-
est daylighting, energy and BIPV area (Figure 5).
CONCLUSION AND DISCUSSION
Energy efficient and sustainable buildings are slowly,
but surely, becoming the standard in architecture
and building practices. As building performance
simulation software and optimization tools become
more common in the building design process, it is
vital to have an integrated result analysis and visual-
ization tool to support the design decisions. This pa-
per presents a prototype for a visualization tool that
can help analyze the results of building performance
multi-objective optimizations. The visualization tool
aids in investigating the whole design set, analyzing
the relation between variables and objectives, as well
as comparing and further investigating preferred de-
signs. As a result, the user can define areas with po-
tential enhancements, find the most effective design
variables and compare the integrated performance
between different designs in a visually-informative
way. By achieving these different functions, the tool
can help in the design decision process by shortening
the time required to analyze the vast amount of data
resulting from multi-objective optimization. In future
works, other enhancements could be investigated,
such as building the tool with a more sophisticated
programming language like Python or Java, support-
ing dashboard customization, and validating the tool
by focus groups. Implementing a guiding system can
also be a valuable addition to the prototype to ensure
that an optimal performance is reached by showing
possible areas of enhancement.
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[1] http://apps1.eere.energy.gov/buildings/EnergyPl
us/
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  • L Beltran
  • De Herde
  • J Hensen
  • M Hamdy
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Attia, S, Beltran, L, de Herde, A and Hensen, J 2009 'Architect Friendly: a comparison of ten different building performance simulation tools', Building Simulation 2009 Proceedings of the Eleventh International IBPSA Conference, Glasgow, Scotland Attia, S, Hamdy, M, O'Brien, W and Carlucci, S 2013, 'Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design', Energy and Buildings, 60, pp. 110124
Solution Analysis in Multi-Objective Optimization' , Proceeding of BSO12 the First Building Simulation and Optimization Confer
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Brownlee, A and Wright, JA 2012 'Solution Analysis in Multi-Objective Optimization', Proceeding of BSO12 the First Building Simulation and Optimization Confer
Architect Friendly: a comparison of ten different building performance simulation tools
  • S Attia
  • L Beltran
  • De Herde
  • J Hensen
Attia, S, Beltran, L, de Herde, A and Hensen, J 2009 'Architect Friendly: a comparison of ten different building performance simulation tools', Building Simulation 2009 Proceedings of the Eleventh International IBPSA Conference, Glasgow, Scotland
Solution Analysis in Multi-Objective Optimization' , Proceeding of BSO12 the First Building Simulation and Optimization Confer-DESIGN TOOLS -THEORY
  • Brownlee
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Brownlee, A and Wright, JA 2012 'Solution Analysis in Multi-Objective Optimization', Proceeding of BSO12 the First Building Simulation and Optimization Confer-DESIGN TOOLS -THEORY -Volume 1 -eCAADe 35 | 727