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The optimisation of semi-transparent building integrated photovoltaic facades can be challenging when attempting to find an overall balance performance between conflicting performance criteria. This paper presents a three-phase design optimisation method that maximises overall electricity savings generated by these types of facades by simulating the combined impact of electricity generation, cooling load, and daylight autonomy.Two demonstrations are performed, with the difference being that the second demonstration uses an enhanced model for calculating daylight savings that takes into account the use of blinds to counteract glare. For both demonstrations, the three-phase optimisation method significantly reduces optimisation run times. Comparing the design variants evolved by the two demonstrations, the use of the enhanced daylight savings model results in a total electricity savings that is more accurate but in terms of visual differentiation, the difference between the optimized design variants is relatively small.
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81
Evolutionary
Optimisation of Semi-
transparent Building
Integrated Photovoltaic
Facades
Thian-Siong Choo and Patrick Janssen
issue 1, volume 12international journal of architectural computing
82
Evolutionary Optimisation of Semi-transparent
Building Integrated Photovoltaic Facades
Thian-Siong Choo and Patrick Janssen
The optimisation of semi-transparent building
integrated photovoltaic facades can be challenging
when attempting to find an overall balance
performance between conflicting performance criteria.
This paper presents a three-phase design optimisation
method that maximises overall electricity savings
generated by these types of facades by simulating the
combined impact of electricity generation, cooling load,
and daylight autonomy.Two demonstrations are
performed, with the difference being that the second
demonstration uses an enhanced model for calculating
daylight savings that takes into account the use of
blinds to counteract glare. For both demonstrations,
the three-phase optimisation method significantly
reduces optimisation run times. Comparing the design
variants evolved by the two demonstrations, the use of
the enhanced daylight savings model results in a total
electricity savings that is more accurate but in terms of
visual differentiation, the difference between the
optimized design variants is relatively small.
1. INTRODUCTION
With the current global emphasis on sustainable design, there is a trend to
design multifunctional semi-transparent building integrated photovoltaic
(BIPV) facades. Such facades integrate PV cells into conventional facade
glazing systems. Semi-transparent BIPV facades can provide good daylight
availability, reduce the solar heat gain through the building envelope, and
also have the ability to generate electricity to supplement the building’s
electricity consumption. It has been shown that semi-transparent BIPV
facades are effective in improving energy efficiency and reducing the overall
electricity consumption of a building [1][2].
The challenge of designing semi-transparent BIPV facades is to optimize
the multiple conflicting performance criteria. Unlike typical roof mounted
photovoltaic systems, where performance is only focused on the amount of
electricity generated, the design of semi-transparent BIPV facades has an
impact on a wider range of factors, including solar radiation and daylight
penetration into the rooms in the building.To optimize the performance of
such facades, optimisation systems can be used that leverage existing
simulation tools for performance evaluation. However, the types of
simulations that are required are often complex in their own right, and may
take a significant amount of time to execute. Optimisation systems typically
need to execute these simulations many times in an iterative manner and as
a result, optimisation systems can have very long run times, with a single run
possibly taking weeks to complete [3].
In order to test the run time of an evolutionary algorithm, a base-case
optimisation of a semi-transparent BIPV facade was conducted with the
performance criteria being the maximisation of electricity savings taking into
consideration electricity generation, cooling load, and daylight savings.This
base-case optimisation does not use parallel processing but uses the
standard software tools and computer systems available to most practising
architects.A parametric model was created and an evolutionary algorithm
was used to evolve an optimized population of designs.The evolutionary
algorithm ran for almost 14 days.This long run-time is a major barrier for
practitioners to adopt multi-objective optimisation methods into their
workflow [4]. Such run times clearly do not align well with a designer’s
process of working.
This paper proposes a three-phase optimisation method as an
alternative method for the optimisation of semi-transparent BIPV facades.
Section 2 gives an overview of the proposed three-phased method for semi-
transparent BIPV façades. In addition, it describes the design scenario, based
on a typical office façade in Singapore, used in the two demonstrations.
Section 3 presents the first demonstration, which maximizes total energy
savings taking into consideration electricity generation, cooling load, and
daylight savings.Section 4 presents the second demonstration, which also
maximises total electricity savings, but in this case an enhanced model for
83Evolutionary Optimisation of Semi-transparent Building Integrated Photovoltaic Facades
calculating daylight savings is used that takes into account the use of blinds
to counteract glare [5]. Section 5 discusses and compares the results from
both demonstration 1 and 2. Lastly, Section 6 briefly draws conclusions and
highlights avenues for further research.
2.THREE-PHASE OPTIMISATION METHOD
The proposed method is based on a general design method developed by
Janssen and Kaushik [6].This method is adapted to the design of semi-
transparent BIPV facades using an evolutionary optimisation approach.The
method consists of three phases: 1) calibration, 2) optimisation, and 3)
validation as shown in Figure 1. In the calibration phase, simulation models
are selected and simulation programs are configured and tested. Simulation
models that are deemed too slow are replaced by faster proxy models,
which are calibrated in order to ensure that appropriate trade-offs are
achieved between speed and accuracy. In the optimisation phase, the proxy
models are used within the iterative optimisation process in order to
explore design variants with improved performance. Finally, in the validation
stage, selected optimal design variants on the Pareto front are analysed and
evaluated in more detail.Any calibrated proxy models are now replaced by
the more accurate simulation models in order to verify the performance
improvements.
Figure 1: Schematic
of the three-phase
method
84 Thian-Siong Choo and Patrick Janssen
2.1 Performance Objectives
In the tropics, semi-transparent BIPV facades affect electricity savings in
three distinct ways, namely electricity generation, cooling load and daylight
savings. Electricity generation is the electricity produced by the PV cells in
the semi-transparent BIPV facade. Maximising electricity generation will
reduce external electricity consumption from the grid. Cooling load is the
amount of electricity required to cool a room to a set temperature.
Cooling load is affected by the amount of solar radiation entering through
the facade. Minimising solar radiation will reduce the electricity
consumption for cooling. Daylight savings is the amount of electricity saved
by using daylight instead of artificial lighting in order to light a room to a set
minimum illuminance level.The daylight savings is affected by the daylight
autonomy, which is the percentage of occupied hours per year when the
minimum illuminance level can be maintained in a room by daylight alone
[7]. Maximising daylight autonomy will also maximize daylight savings.
In the case of daylight savings,an additional factor that may be taken into
consideration is that occupants will typically close window blinds in order
to counteract glare. Since closing blinds reduces daylight autonomy, daylight
savings will also be reduced.
2.2 Calibration Phase
For each of these components, an appropriate simulation model will be
chosen and tested. If the execution time of the simulation is excessively
slow,a proxy model will then be used that is faster to execute, but that
nevertheless still has sufficient accuracy to guide the evolutionary process.
The total electricity saved is defined by Equation 1:
ES= EG+ DS– CL(1)
where ESis the total electricity saving (kWh·a-1), EGis the electricity
generated (kWh·a-1), DSis the daylight savings (kWh·a-1) and CLis the
cooling load (kWh·a-1).
2.3 Optimisation Phase
For the optimisation phase, an evolutionary algorithm is used to evolve a
population of optimized design variants.The evolutionary algorithm requires
three key procedures to be defined: development, evaluation, and feedback.
The development procedure generates design variants using a parametric
model. Genes in the genotype are then associated with the parameters in
the model.
The evaluation procedure evaluates design variants.For both
demonstrations, the evaluation procedure calculates the total electricity
savings, which includes the daylight savings, cooling load and electricity
generation.The second demonstration uses an enhanced model for
calculating daylight savings that takes into account the use of blinds to
counteract glare.
The feedback procedure kills design variants that perform badly and
reproduces design variants that perform well.The evaluation scores for the
85Evolutionary Optimisation of Semi-transparent Building Integrated Photovoltaic Facades
different performance criteria are combined to create a single fitness score
as shown in Equation 1.
2.4 Validation Phase
For the validation phase, the best design variants that emerge from the
evolutionary process are analysed and re-simulated.At this final stage, the
slower but more accurate original simulations should replace the proxy
simulations. Performance characteristics of the final design variants can then
be verified.
2.5 Demonstrations
Both demonstrations involve optimising the pattern of PV cells on a semi-
transparent BIPV facade in order to maximize the total electricity savings as
shown in Figure 2.The PV pattern affects both the solar radiation and the
daylight penetrating into the room through the glazing.
A typical north oriented office space for one person occupancy with 4
m (width) x 4 m (depth) x 3 m (height) is modelled for the experiment, as
shown in Figure 2 (top left).The facade is separated into four zones: vision
glass panels 1, 2, 3 and a spandrel glass panel. Each zone is independent from
one another.Three discrete valued genes – cell height, cell width and cell
spacing – define the PV cell pattern for each independent zone, as shown in
Figure 2 (bottom left). Cell height and width vary from 5 – 15.5 cm at 0.5
cm steps. Cell spacing varies from 0.5 – 5 cm at 0.5 cm steps.All the cells of
the semi-transparent BIPV facades will be similar in shape.The pattern
occupies a facade with a height and width of 4 m.
Figure 2.Top Left:Simulation model
with sensors (a = 1.50m, b = 0.85m, c
= 0.50m). Bottom Left: Schematic of
cell arrangement for semi-transparent
BIPV façade with gene 1_x, 2_x and
3_x, where x is the glass panel
numbers (1-3). Right. Section of a
typical office space.
86 Thian-Siong Choo and Patrick Janssen
For the first demonstration, the model used for calculating daylight
savings assumes that no blinds are used. For the second demonstration, the
enhanced daylight savings model assumes that occupants will close the
blinds to counteract visual discomfort due to glare.A roller blind with an
openness factor of 3 is modelled behind vision glass panel 2 as shown in
Figure 2.The blind is activated whenever the daylight glare probability is
above 0.35, which is when glare is perceptible [8].
2.6 Software Tools
The three-phase method shown in Figure 1 is not dependent on any
particular software. It can be implemented with a range of different
software tools for parametric modelling and performance simulation. For
the two demonstrations, commonly used software tools for six different
tasks were considered.They are 1) parametric modelling; 2) performance
simulation of electricity generation, EG,of BIPV system; 4) performance
simulation for daylight savings, DS;3) performance simulation of cooling
load, CL;5) performance simulation of daylight glare probability and 6)
evolutionary algorithm for design optimisation.
For both demonstrations, the same sets of software tools are used. For
parametric modelling, Grasshopper was chosen mainly due to its popularity
in the design community [3]. Grasshopper is a plugin for the Rhinoceros
computer aided design modelling software [9]. It is a Visual Dataflow
Modelling (VDM) system that allows designers untrained in scripting to
generate parametric models quickly [10].
A number of specialist Grasshopper components and custom
components listed in Table 1 are used for running optimisation algorithms
and executing simulations. For evolutionary optimisation, the Galapagos
component is used [3].This component is an evolutionary optimisation
solver, which can be used to optimize designs for single fitness metrics. For
electricity generation, a simplified model is implemented in Grasshopper as
a mathematical equation. For cooling load, a model is defined that uses the
DIVA-Viper Grasshopper component that links to the EnergyPlus energy
simulation engine [11][12]. For the daylight savings model, a model is defined
Table 2: Selected
software for
demonstration 1 and 2.
87Evolutionary Optimisation of Semi-transparent Building Integrated Photovoltaic Facades
Processes Selected Software
Parametric Modelling Grasshopper (GH)
Evolutionary Algorithm Galapagos (a GH component)
Simulation for EG Custom GH component
Simulation for DS DIVA-Daylight (a Grasshopper plugin that has a wrapper to DAYSIM)
Simulation for CL DIVA-Viper (a Grasshopper plugin that has a wrapper to EnergyPlus)
Simulation of DGP Custom GH component developed as a wrapper to Evalglare
that uses the DIVA-Daylight component that links to the Daysim simulation
engine [7][11]. In this case, Daysim is used to calculate daylight autonomy,
which is then used as a basis for calculating daylight savings. Both the
cooling load model and the daylight savings model use the EnergyPlus
weather data file for Singapore [13].
For the first demonstration, Figure 3 shows the workflows for the
various software tools and how they relate to the three-phase method. For
the second demonstration, an additional customized simulation component
is developed within Grasshopper to link to Evalglare, a radiance-based tool
for glare simulation and analysis [5].The addition of the Evalglare simulation
is shown in Figure 6 in Section 4.
Figure 3:Workflow of different
software tools for demonstration 1
88 Thian-Siong Choo and Patrick Janssen
3. DEMONSTRATION 1
The first demonstration calculates electricity savings by taking into
consideration electricity generation, cooling load and daylight savings. In this
case, the model used for calculating daylight savings assumes that no blinds
are used.
3.1 Calibration Phase
For the calibration phase, the three components of the total electricity
savings calculation will be explained below.
Electricity Generation Model
In order to obtain a precise prediction of the annual electricity generation
of any particular photovoltaic module, the module first needs to undergo
extensive testing in the laboratory in order to obtain a set of electrical
characteristics. Using this data, EnergyPlus can then be used to simulate
annual electricity generation using a model called the ‘equivalent one diode
model’ [14].
In terms of execution time, the equivalent one diode model in
EnergyPlus is sufficiently fast. However, in this case, the problem is that the
electrical characteristics of the photovoltaic modules being evolved are not
known.The developmental process generates alternative configurations of
photovoltaic modules and it is therefore clearly impossible to test these in a
laboratory.A simplified model is therefore used that can estimate the annual
electricity generation with reasonable accuracy using the mathematical
equation shown in Equation 2:
P = Asx Fax Gtx Effcell x Effinvert (2)
where P is the electrical energy produced by photovoltaic modules (kWh),
Asis the net area of photovoltaic module (m-2), Fais the fraction of surface
area with active solar cells, Gtis the total annual solar radiation energy
incident on BIPV (which is set at a DIVA-calculated value of 561 kWh.m-2),
Effcell is the semi-transparent BIPV facades module efficiency (which is set at
12%) and Effinvert is the average inverter efficiency (which is set at 90%).To
find Gt,the annual solar radiation energy incident on the BIPV, a one-time
simulation is done with DIVA-Daylight.
In order to verify the accuracy of the simplified model, a set of
commercially produced photovoltaic modules (for which the electrical
characteristics were already known) were simulated using both the
equivalent one diode model in EnergyPlus and the simplified model. In total,
simulations for 16 different modules for the four different cardinal
directions were carried out,resulting in 64 variants.The two sets of data
were plotted on a graph using Microsoft Excel and a linear trend line was
calculated.This resulted in an R2coefficient of determination of 0.98,
thereby confirming that the simplified model has a reasonably good
correlation with the equivalent one diode model in EnergyPlus.
Daylight Savings Model
Daylight savings is calculated based on the daylight autonomy for the room,
which can be simulated using DIVA-Daylight. Since the Daysim simulation in
DIVA-Daylight is already an optimized simulation model, the simulation
executed relatively quickly and there was no need to create a proxy in this
case [7][11].
With reference to a recent study on various lighting standards around
the world by Halonen et al, it was found that minimum illuminance for
interior spaces ranges from 200lx to 500lx [15]. Hence, for the simulation
of daylight autonomy, the minimum illuminance level of 500lx is set for the
simulation.Working hours are set from 9:00 to 18:00.A 3 x 3 nodal grid of
daylight sensors are drawn 0.85m from the floor and 0.25m away from the
vertical walls (Figure 2, top). Since daylight autonomy is more critical for
89Evolutionary Optimisation of Semi-transparent Building Integrated Photovoltaic Facades
areas further way from the windows,only the back 2 rows of 6 daylight
sensor nodes are used for the daylight autonomy simulation.
Default materials from the material library in DIVA-Daylight are used for
the simulation. Based on research done by, which shows that a typical
photovoltaic module has a reflectance of below 10%, the photovoltaic layer
is assigned a reflectance of 10% [16].
The following settings were used in DIVA-Daylight: ab = 2, ad = 1000, as
= 20, ar = 300 and aa = 0.1, where ab is ambient bounce, ad is ambient
divisions, ar is ambient resolution and aa is ambient accuracy.The detailed
explanation of the settings is beyond this paper.They can be found in the
Radiance manual. Daylight savings were then calculated according to the
following formula:
DS= (DAsim/100) * LPB * FA * WH (5)
where DSis the total daylight savings (kWh·a-1), DAsim is the simulated
daylight autonomy (%),LPB is the lighting power budget (kW.m-2), FA is the
floor area of simulation model (which is 16m2) and WH is the working
hours per year. LPB is set based on the Code of Practice, which
recommends an LPB for offices of 0.015kW.m-2 [17]. WH is set based on 5
work days per week with 9hrs of work per day, which results in 2,340hrs
per year.
Cooling Load Model
The cooling load for the room is simulated using DIVA-Viper. For
simplification, the study considers the heat gain through the semi-
transparent BIPV facades but not the internal heat gains from lights,
equipment and occupants. Default materials from the material library in
DIVA are used.With reference to Figure 2, the walls, floor and ceiling are
assigned as “adiabatic” and spandrel glass panels are assigned as “opaque
spandrel glass”.A window module is defined using a typical 6 mm thick clear
glass window with a U-value of 5.8, a solar heat gain coefficient (SHGC) of
0.82 and a visible transmittance (VT) of 0.88 [18].
Since the PV cells are on the surface of the glass, they will reduce the
solar radiation entering the window,essentially acting as small shading
devices.There are two different approaches to modelling this shading effect
with different trade-offs between speed and accuracy.The slower but more
accurate approach is to model PV cells as individual shading elements.The
calculation of the amount of solar radiation entering the window is
dependent on the time and location of the sun, and it therefore needs to be
simulated for each time-step for an entire year. Depending on the design
variant, the total number of PV cells can range from 156 to 4224. Each cell
is assigned a solar reflectance of 0.1 and visible reflectance of 0.1.The large
number of shading elements resulted in a relatively long time taken for the
simulation to run, with the longest simulation taking approximately 30
90 Thian-Siong Choo and Patrick Janssen
minutes.This caused the optimisation of the base case, mentioned in the
introduction, to run for almost 14 days.
In view of the slow simulation speed, a faster proxy model is used.With
this approach, the solar heat gain coefficient (SHGC) and visible light
transmittance (VLT) for the facade are adjusted to take into account the
effect of the PV cells.The equations for SHGC and VT used in the proxy
simulation are shown in Equations 3 and 4:
SHGC = Apv/A x SHGCvg (3)
where SHGC is the solar heat gain coefficient of semi-transparent BIPV
facade (Wm-2K-1),Apv is the total area of PV cells (m2),A is the area of
semi-transparent BIPV facade (m2), and SHGCvg is the solar heat gain
coefficient of the vision glass panel (which is part of the semi-transparent
BIPV facade but without photovoltaic cells) (Wm-2K-1).
VT = Apv/A x VTvg (4)
where VT is the visible transmittance of the semi-transparent BIPV facade
(Wm-2K-1),Apv and A are as in Equation (3), and VTvg is the visible
transmittance of the vision glass panel (which is part of the semi-
transparent BIPV façade but without PV cells).
In order to check the accuracy of the proposed proxy model, 164
cooling load simulations for different BIPV facades were conducted using
both the accurate simulation and the proxy model.The two sets of data
were plotted on a graph using Microsoft Excel and a linear trend line was
calculated.This resulted in an R2coefficient of determination of 0.93,
thereby confirming that the simplified model has a reasonably good
correlation with the slower and more detailed cooling load simulation.
3.2 Optimisation Phase
For executing the evolutionary algorithm, the Grasshopper Galapagos
component is used. Galapagos is executed with a population of 30, initial
boost of 2%, and the maintain level is set at 10% and inbreeding at 75%.The
system was executed on a single computer with an i5 Intel core CPU of
3.5GHz with 8GB of RAM, on 64 bits Windows.
The optimization process ran for 2 days,17 hours, generating a total of
1471 design variants.The design variant with the best fitness is variant
number 753, with a total electricity savings of -131 kWh·a-1 (shown as a
black dot on Figure 5).
91Evolutionary Optimisation of Semi-transparent Building Integrated Photovoltaic Facades
3.3 Validation Phase
In the final validation phase, a set of designs from the Pareto front were
selected and analysed (see Figure 5). In order to verify the performance
improvements in cooling load, these designs were re-simulated using the
slow cooling load simulation. In order to better understand the
relationships between the design variants, all designs are ranked using
Pareto ranking, and those on the Pareto front are then plotted on a 3D
graph (see Figure 5 left). On this graph, the optimal design is shown as a
black dot and certain selected other designs are shown as grey dots.
In all cases, the results from the slow mode simulation confirmed the
performance improvements. Compared to the base case design shown in
Figure 4, the optimal design variant shown in Figure 5 has an overall
improvement for the total electricity savings of 61%.
Figure 4. Left: Base case design
variant with standard PV cell
arrangement. Right: Bar chart of ES,E
G,
DSand CLfor base case design variant.
Figure 5. Left: 3D plot of results for
optimisation assuming no blinds are
used. Right:
design variant 753, the optimized
design variant for demonstration 1.
92 Thian-Siong Choo and Patrick Janssen
4. DEMONSTRATION 2
The second demonstration calculates total electricity savings using the same
approach as in the first demonstration, taking into consideration electricity
generation, cooling load, and daylight savings. In this case, an enhanced
daylight savings model is used.
4.1 Calibration Phase
For electricity generation and cooling load, exactly the same models are used
as in the first demonstration. For daylight savings, an enhanced model is
developed that assumes that occupants will close the blinds to counteract
visual discomfort due to glare.This will reduce the daylight savings that can be
achieved since closing blinds will reduce daylight autonomy.The modified
equation for calculating the total electricity generated is shown in Equation 6.
ES= EG+ DS*– CL(6)
where ESis the total electricity saving (kWh·a-1), EGis the electricity
generated (kWh·a-1), CLis the cooling load (kWh·a-1), DS*is the enhanced
daylight savings (kWh·a-1).The enhanced daylight savings model is described
in more detail below.
Enhanced Daylight Savings Model
For enhanced daylight savings, the model must take into account the use of
blinds to counteract glare. One approach to this would be to perform an
annual simulation that calculates glare at every point in time. However, a
base-case simulation took approximately 6 hours and as a result this
approach was clearly too slow.
A proxy model is therefore used that calculates a reduction factor to
daylight savings.The reduction factor is defined by the glare coefficient, α,
which is added as a factor to daylight savings, DS:
DS*= αDS(7)
where DS*is the enhanced daylight savings (kWh·a-1) taking into account
glare, DSis the daylight savings (kWh·a-1), and αis the glare coefficient.The
full equation for calculating enhanced daylight savings is shown in Equation
8, and with the exception of the glare coefficient, it is exactly the same as
Equation 5 used by the daylight savings model for the first demonstration.
DS*= (αDA/100) *LPB *FA *WH (8)
Glare Coefficient
The key to the enhanced daylight savings model is therefore calculating the
glare coefficient.As a starting point to calculating this coefficient, it is noted
that it may be considered to be a factor that reduces daylight autonomy due
to the use of blinds.Therefore, if daylight autonomy can be calculated both
with blinds and without blinds, then the appropriate factor for reducing
93Evolutionary Optimisation of Semi-transparent Building Integrated Photovoltaic Facades
daylight autonomy can be calculated.This factor would then be the glare
coefficient.
Glare can be approximated using a measure called ‘daylight glare
probability’ (DGP), which represents the probability of people being visually
disturbed due to the following factors: level of vertical eye illuminance,
luminance of glare source, solid angle of glare source and position index.
Glare is considered imperceptible when DGP is less than 0.35. Hence for
the enhanced daylight autonomy calculation,it is assumed that the blinds
will be close at all times when the DGP rises above 0.35.
DGP can be simulated using the Evalglare program [5]. Since Evalglare is
not available as a Grasshopper component, a customized Grasshopper
component has been developed by Choo et al [19].This allows DGP to be
easily calculated at any point in time for any design variant. However, the
problem is once again that the annual simulation is very slow, and calculating
the annual DGP for every point in time for a whole year would be
prohibitively slow. In order to overcome this, DGP for a whole year can be
feasibly calculated for a range of design variants with just a single point in
time. In such a case, it is important to choose a point in time that is
representative of a range of design variants and different glare conditions.To
determine this point in time, 20 runs of the annual glare simulation were
performed for a range of design variants in order to determine the day and
time when the blinds for all design variants were subject to a DGP of
greater than 0.35.The selected point in time was the 21st June, 12:00PM
which is the solstice.
The methodology for calculating the glare coefficient can therefore be
summarised as follows. First, a set of 125 design variants were generated
from the parametric model. For each of these design variants, daylight
autonomy without blinds and daylight autonomy with blinds was calculated
for the 21st June, 12:00PM.The two sets of results for daylight autonomy
(without blinds and with blinds) were then plotted on a graph in Excel and a
linear regression was then performed.The glare coefficient is then equal to
the regression coefficient, which in this case was 0.69.
Figure 6 shows the workflows for the various software tools, including
Evalglare.This figure may be compared to Figure 3.
94 Thian-Siong Choo and Patrick Janssen
4.2 Optimisation Phase
The same settings are used as for the first demonstration. Galapagos is
executed with a population of 30 and initial boost of 2%.The maintain level
is set at 10% and inbreeding at 75%.The system was executed on a single
computer with an i5 Intel core CPU of 3.5GHz with 8GB of RAM, on 64
bits Windows.
Figure 6:Workflow of different
software tools for demonstration 2,
including the Evalglare software tool.
Figure 7: Left: 3D plot of results for
optimisation with enhanced daylight
savings. Right: Optimized design variant
for demonstration 2.
95Evolutionary Optimisation of Semi-transparent Building Integrated Photovoltaic Facades
The optimization process ran for 14 days 17 hours,generating a total of
1771 design variants.The design variant with the best fitness is variant
number 861, with a total electricity savings of -674 kWh·a-1 (see Figure 7
right).As with the previous demonstration, a 3D Pareto graph is plotted in
order to better understand the relationships between the design variants
(see Figure 7 left).
4.3 Validation Phase
Similarly to demonstration 1, in the final validation phase, the designs from
the Pareto front were selected and analysed (shown as grey dots on Figure
7).The results confirmed the performance improvements with respect to
the base case shown in Figure 4.
5. DISCUSSION
The results from the two demonstrations are compared at three different
levels: in terms of the performance of design variants; in terms of the visual
differentiation of the design variants; and in terms of the speed of execution
of the optimisation algorithm. In order to aid in the comparison of design
variants, selected designs from the Pareto fronts for the two
demonstrations are shown in Figure 8.
5.1 Performance of design variants
The total electricity savings has a range from worst to best of - 2,102
kWh·a-1 to -131 kWh·a-1.For the first demonstration, the total electricity
savings for the design variants on the Pareto front ranged from -131 kWh·a-
1 to -1,104 kWh·a-1.For the second demonstration, the total electricity
savings for the design variants on the Pareto front ranged from -647 kWh·a-
1 to -1,682 kWh·a-1.The base case shown in Figure 4 has a total electricity
savings of -795 kWh·a-1.
The key observation from these results is the significant and consistent
drop in the fitness score for the second demonstration.This is primarily due
to a drop in daylight autonomy as a result of occupants closing blinds to
counteract glare. For the first demonstration, the best design variant had a
daylight autonomy of 68.2%,while for the second demonstration, the best
design variant had a daylight autonomy of only 18.7%.This reduction in
daylight autonomy is the main reason why the best design variant from the
first demonstration has a total electricity savings that is 81% greater than
the best design variant from the second demonstration.
This highlights that without considering glare in the optimisation
process, there is an overestimation of 81% of total electricity savings.The
second demonstration using the enhanced daylight savings model therefore
gives a more accurate indication of final performance that is closer to the
reality.
96 Thian-Siong Choo and Patrick Janssen
Figure 8.Top:Selected design variants on the Pareto front and corresponding performance bar charts for demonstration 1. Bottom:
Selected design variants on the Pareto front and corresponding performance bar charts of demonstration 2.
97Evolutionary Optimisation of Semi-transparent Building Integrated Photovoltaic Facades
5.2 Visual differentiation of design variants
Besides considering the building performances, the aesthetical implications
of the different design variants are also likely to be of interest to designers.
In general, the design variants on the Pareto front vary considerably from
one another. For example, Figure 9 shows two design variants from the
second demonstration; design variant 27 shows a more open view out of
the space compared to design variation 861.The designer may decide to
choose design variant 27 despite the lower performance.
Figure 9. Left: Interior perspective
of the 861st design variation from
demonstration 2. Right: Interior
perspective of the 27th design
variation from demonstration
98 Thian-Siong Choo and Patrick Janssen
With respect to visual differentiation, it is noteworthy that for the
optimal design from the first and second demonstrations, the difference
between the two design variants is relatively small, with both designs having
very densely packed PV cells (see Figures 5 and 8).This suggests that it may
not be necessary to use the enhanced daylight savings model that takes into
account occupants’ use of blinds, since in the end, both daylight savings
models converge on similar designs.
5.3 Speed of Execution
For both demonstrations, the use of the three-phase method led to
significant reduction in run times. In the case of the first demonstration, a
base-case optimisation was run.The total run time for the base case was 14
days, while the total run time using the three-phase method was 2 days and
17 hours.This is a reduction of 81%.
In the case of the second demonstration, no base case was run as it
would have taken a prohibitively long time. Based on the execution times of
the individual simulations, it is clear that an optimisation run not using proxy
models would have taken years to complete.The total run time using the
three-phase method was 14 days and 17 hours.
Comparing the two demonstrations, the run times using the three-phase
method differed significantly.The enhanced daylight savings model,and in
particular the Evalglare simulation used within that model resulted in a
543% increase in run time.
This increase in run time further brings into question the need for the
enhanced daylight simulation model. If a more accurate prediction total
electricity savings is required,it would still be possible to optimize using the
normal daylight savings model (as performed in the first demonstration), and
to then re-simulate the final selected designs using the enhanced daylights
savings model.
6. CONCLUSION
The use of the three-phase method for optimizing semi-transparent BIPV
facades with conflicting performance objectives has been demonstrated.The
two demonstrations highlighted the complexities involved in trying to
reduce the run time of the evolutionary optimisation algorithms.The results
from the demonstrations suggest that the most feasible approach may be to
optimise designs without taking into account the use of blinds, and then to
simulate the use of blinds to counteract glare during the validation stage.
However, even without taking into account the use of blinds,the run time of
the evolutionary algorithm was still over two and a half days long. For most
designers, this would be considered very long, especially if the intention was
to apply these methods in early stage design. In order to ensure that such
optimisation methods can be effectively applied, both the complexity of
setting up the models and the overall run times of the optimization
algorithms will need to be significantly reduced. Future research will
investigate parallel execution of the optimisation algorithm and replacement
of the simulations with surrogate models.
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100 Thian-Siong Choo and Patrick Janssen
Thian-Siong Choo and Patrick Janssen
Department of Architecture
School of Design and Environment
National University of Singapore
4 Architecture Drive
Singapore 117 566
Thian-Siong Choo, thiansiong.choo@nus.edu.sg, choothiansiong@yahoo.co.uk;
Patrick Janssen, patrick@janssen.name
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