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High|Bombastic is part of a research on Living Building Envelopes still in progress at Mailab, a research center active for 7 years at the University of Florence on Multimedia, Architecture, and Interaction.
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Computational BIPV Design : An energy optimization tool for solar facades
Omid Bakhshaei, independent researcher and co-founder of Novintarh Studio, 2nd floor, 193, Molla Sadra street,
Tehran, Iran. bakhshaei.omid@gmail.com
Giuseppe Ridolfi, professor at the Department of Architecture (DIDA), Università degli Studi di Firenze, via della
Mattonaia n.14, Florence, Italy. giuseppe.ridolfi@unifi.it
Arman Saberi, doctorate researcher at the Department of Architecture (DIDA), Università degli Studi di Firenze,
via della Mattonaia n.14, Florence, Italy. arman.saberi@unifi.it
___________________________________________________________________________________________
ABSTRACT
In contemporary buildings, facades are generally the largest borders between the inside and the outside which
determine the proportion of energy consumption of the buildings. With today’s technology, they could also offer
the opportunity of producing energy by adding photovoltaics into their systems, to cover a portion of the building’s
need for electricity and reduce its dependency on fossil fuels, especially where there is a high amount of Global
Horizontal Irradiation (GHI) and a high potential for generating electricity from photovoltaics. In the new concepts,
BIPV is even being used in the transparent sections of the facades which should be a cautious decision as they
can highly affect the total energy demand of the building due to the change in the proportion of daylight and heat
that can pass through. Thus they should all be taken into consideration during the first stages of design to get the
best result possible. While there have been some studies on this subject we are still facing a shortage of tools and
methods for BIPV design in the preliminary design phases.
This research aimed to provide a design tool for BIPV systems by making use of the integration of energy
simulation programs with visual programing tools to spot the best façade solutions for any specific project. The
optimization of these solar façades by this tool is discussed and compared to the non-optimized alternatives. To
put it briefly, the tests were done on a common vertical two section façade with windows to provide natural light
and solar heat to a certain amount that would be beneficial energy-wise, with crystalline silicon based photovoltaics
in the remaining parts of the facades.
The simulation results illustrated how a great quantity of inputs could affect the performance to a great extent. For
instance, glazing material was put on a test and the results with four different alternatives in south façade of Cairo
(Egypt) showed that a wrong decision on glazing material alone could result in an increase of 28% in lower WWRs
and 51% in higher WWRs in the energy consumption of an office room. Therefore, by choosing the optimal solution
for each input we could reduce the energy use of a building extensively which highlights the need for tools to come
to the aid of the decision makers to find the best options and avoid choosing theinferior alternatives during the
first stages of design.
Topic: Renewable Energies in Building & Cities
Keywords: Energy optimization tool; Building Integrated Photovoltaics (BIPV); Solar Façades; Passive design
1.INTRODUCTION
Throughout history Architects have always aimed to define some methods which were gained from their
experience to achieve designs that lead to higher performance buildings, but today with the emergence of energy
simulation programs which can simulate the performance of buildings in terms of heating and cooling electricity
consumption within 3% of mean absolute error [1], and with the high degree of complexity in projects, practicing
rules of thumb would be a failure in catching up with the developments in the field.
There have been previous studies on using simulation tools for the optimization of solar facades with different
methods. This was done by providing a tool to optimize the geometry of the building to achieve maximum insolation
[2] and focusing on maximizing the amount of energy production rather than minimizing the net energy need or by
trying to optimize energy production by finding the façade parts that have the highest insolation for BIPV positioning
[3]. There have also been some studies on the PV area ratio on glazing to provide light and energy production at
the same time and reach the lowest net energy consumption [4], [5]. In this research minimizing also the
dependency to fossil fuels was considered as the goal rather than maximizing the production of renewable energy;
thus the solutions which had the lowest net energy need were considered optimum.
In producing an optimizing tool for solar façade design, the first task would be to find the variables that could
change a façade’s performance for better or worse. One of the main effective parameters in changing the amount
of energy loads of buildings is the size of windows. Nowadays, facades with vast glazing are more common to
make the best use of daylight, which could result in higher loads due to the lower capabilities of glass to act as a
heat insulator in comparison with walls in general. Therefore, they should have high performance materials, or
preferably a reasonable size to provide the required daylight level while keeping the amount of heat transfer at its
best rate.
There are other variables that could change the amount of solar gain and as a result the optimum window-to-wall
ratio. An adjustment in the material properties of the walls or the windows themselves such as solar heat gain
coefficient (SHGC) or U-value or whether the windows have external blinds or not could change the amount of
energy loads and the ideal WWR to a great extent. Other factors like geographical location, orientation, the
required light level inside of the building, etc., would also affect the optimum window-to-wall ratio for facades. In
designing these solar façades where building integrated photovoltaics (BIPV) are a part of their system, the amount
of energy that photovoltaics produce would also be added as a factor that plays a role in determining the optimized
WWR.
2. METHODOLOGY
It was decided to study the effectiveness of the variables on the optimal solar façade solution by testing them on
different WWRs to monitor the changes. Consequently, the Enumerative method was used for the tests. The
principle of this method is simple. Within a finite search space, or a discretized infinite search space, the algorithm
assesses the fitness function at every point in the space, one at a time [6]. In this way a better understanding of
the impact of the variables could be reached.
2.1. Inputs
A list of all effective parameters on the ideal façade solution was gathered as changing variables for the
simulations. A detailed list of the provided inputs is demonstrated in the following Table 1.
Geometry Material Properties (Wall) Material Properties (Glazing)
Length Function U-value U-value
Width Lighting set point PV to wall ratio SHGC (Solar heat gain coefficient)
Height Shading scenario PV Efficiency VT (Visible transmittance )
Orientation PV temperature coefficient PV Efficiency
Location PV temperature coefficient
Table 1. Required Inputs
Function: By choosing the function input, visual properties of the surfaces, equipment and lighting power density,
or thermal loads conditions like ventilation and occupancy schedules would be set automatically by predefined
values based on the given function.
Lighting set point: This input sets the needed amount of light. When there is a lower light level than the set point
input, it means there is a shortage of daylight, and the artificial lighting system will fill the gap with its automatic
dimming controls.
Shading scenario: By selecting True for the shading scenario, an optimized horizontal blind system would be
calculated for the windows (based on the other inputs selected) at the starting point of the calculations, and it
would be considered in the evaluations.
Each input could take more than one value that would be tested in the later rounds of the calculations. For instance,
in the second round, all the second inputs would be taken into account altogether, and for the third round, the third
inputs and so on. Where there is no value for the second test for an input, the tool will automatically use the same
last value for the new round. The existence of any multiple values for one input would result in multiple calculations.
2.2. Predetermined inputs for the evaluations
In the calculations of this article, there are some fixed and some changing values as inputs and not all inputs would
be tested and considered as variable. Three different cities with different climates in the Mediterranean region
were selected as locations for a typical office room with an area of 35 m2 and geometry inputs of 7m width, 5m
depth, and 4m clear height, which was the case study for all the evaluations. (Figure 1)
Figure 1. Geometry of the case study Figure 2. Context’s scenario
This single office is included in an entire building; thus, only one wall would be fully exposed to the outdoor space.
(Figure 2) This external wall consists of a single window placed at its center, as the energy load of the office would
be at its lowest when the windows are located in middle height [7]. The WWR test range is from 1% to 100%.
For the Artificial lighting, a set point of 500 Lux, which is the minimum level for comfort in offices according to EN
1246-1 [8], was given as input to the auto-dimming system. The reference plane of the daylight simulations was
placed at a working plane of 0.8m height with a sensor dedicated to each square meter which would be a default
action in the provided tool. In Figure 3 the arrangement of these sensors in this office space is demonstrated.
Figure 3. Arrangement of the daylight sensors
2.3. Evaluation tools
All the simulations are carried out in Grasshopper, Ladybug and Honeybee which are all Rhinoceros3d plug-ins
and they are all used to interface EnergyPlus [9] and Radiance [10] for the annual energy and illuminance
computations.
EnergyPlus is a free and open source building energy simulation program. Its development is
funded by the US Department of Energy that is open source. Radiance is also an open source program, which
is the most generally useful software package for architectural lighting simulation [11]. Even though Radiance is
not a common tool for architects due to the lack of a graphic user interface, and as it needs an accurate model for
simulation purposes, Grasshopper which is a visual programming tool was used as the modeling software.
3.Results and Discussions
3.1. Adding photovoltaics to the wall’s system
For the first evaluation, the effectiveness of different WWR’s on energy consumption of the office with a simple
wall with no photovoltaics was compared with a BIPV wall system. In the second wall system, where BIPV was
used, all the area of the façade had photovoltaics added to its system except for the transparent section; thus the
PV to wall ratio was equal to one minus the WWR value. The other inputs that were used for this simulation are
illustrated in Table 2.
Geometry Material Properties (Wall) Material Properties (Glazing)
Orientation : South U-value : 0.34 U-value: 2.56
Location : Cairo PV Efficiency: none, 14% SHGC: 70
Shading scenario: True, True PV temperature coefficient: none , 0.45 VT: 80
Table 2
.
Inputs considered for the first test
Figure 4. Energy consumption based on Window-to-Wall Ratio (WWR) for different wall materials
Location: Cairo, Egypt; Orientation: South
0
1000
2000
3000
4000
5000
6000
7000
8000
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
67
70
73
76
79
82
85
88
91
94
97
100
TotalAnnualLoads(KWh/year)
WWR(%)
NormalWall BIVPWall Optimalvalue
The results in figure 4 show how the ideal WWR value changes by adding photovoltaics to the façade system.
While the best WWR for a normal façade with the mentioned properties was 15%, this ideal ratio stepped down to
8% after adding the photovoltaics. This change is due to an added factor which is the energy production by the
photovoltaics. The higher they produce energy the lower the optimal WWR becomes.
3.2. Adding photovoltaics to the Glazing’s system
After photovoltaic was added to the wall’s system in the first simulation, the effectiveness of different materials for
glazing on the total energy consumption and the ideal WWR was put to the test. Three photovoltaic glazing types
with different properties were chosen for a better investigation, as adding photovoltaics to the transparent part of
the façade would be more complex in comparison with adding them to the opaque parts. The properties of these
glazing materials are demonstrated in Table 3 as inputs along with the others. Other inputs remained the same as
the previous test for the BIPV wall.
Properties Normal Glass with
optimized blinds
BIPV 1 BIPV 2 BIPV 3
U-value (W/m2K) 2.56 1.2 1.2 1.2
SHGC (%) 70 30 20 10
VT (%) 80 50 30 10
PV Efficiency (%) - 2.8 3.4 4
PV temperature
coefficient(%/oC)
- -0.13 -0.13 -0.13
Table 3. Inputs considered for the glazing in the second test
Figure 5. Energy consumption based on Window to Wall Ratio (WWR) for different glazing materials
Location: Cairo, Egypt; Orientation: South
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
7000
1 4 7 101316192225283134374043464952555861646770737679828588919497
TotalAnnualLoads(KWh/year)
WWR(%)
NormalGlass BIPV1 BIPV2 BIPV3 OptimalValue
The results in Figure 5 show how the optimal value of WWR changes for each material. From 8% which belongs
to the previous test results with only wall as a source of energy production, to 12% for the first BIPV alternative,
17% for the second BIPV, and 2% for the third. The results illustrate how a wrong choice for the glazing material
could result in higher energy load. For instance, by choosing BIPV 3 instead of the normal window at WWR of 8%,
655.64 kWh would be added to the annual energy consumption of the office which would be a 28% increase.
The graph also shows how each material has its own best WWR range which would be beneficial while it would
be a wrong pick for other situations. For example, choosing the same BIPV 3 instead of normal glass in WWR of
93% where BIPV3 would be the best choice, energy consumption of the office drops from 6407.36kWh per year
to 4210.96 kWh, which would be a decrease of 34%. The Figure 6 shows the most beneficial glazing material for
different window-to-wall ratios.
Figure 6. Best glazing material based on energy consumption for different WWRs
Location: Cairo, Egypt; Orientation: South
For the first 8%, normal glass with blinds would be the best solution, from 9% to 17% BIPV1, from 18% to 92%
BIPV2 and from 93% to a whole glass façade, BIPV3 had the best results in comparison to other materials provided
as inputs in this test.
It can be seen in Figure 5 and 6 that highest range of WWR for being the best material belongs to the second
alternative of BIPV’s, while the optimized value belongs to the first (at 12% with a total energy load of 2345 kWh).
It is worth mentioning that the energy-wise optimal value would not always be the proper solution. By having
access to this valuable data, decision makers could decide what would be the optimal value according to their own
requirements. For instance, the optimal value of WWR for normal glass is 8% with total energy load of 2368.5
kWh. Generally, the preference would be bigger windows and more natural light inside. The decision maker could
expand the WWR without sacrificing net energy consumption. By choosing the BIPV1 for instance, WWR could
rise to 14% with total energy load of 2361 kWh which is even less than the consumption of normal glass at 8%,
while the amount of lighting energy would have a decrease of 26% due to having a higher amount of daylight.
Material Cooling
consumption
Heating
consumption
Lighting
consumption
PV
production
Total
Normal Glass with Blinds
WWR = 8%
4573.24 712.87 362.64 3098.54 2368.53
BIPV 1
WWR = 14%
4578.31 694.61 267.23 2997.13 2361.34
Table 4. Comparison of two different alternatives for glazing
Location: Cairo, Egypt; Orientation: South
0
20
40
60
80
100
WWR(%)
NormalGlass BIPV1
BIPV2 BIPV3
3.3. Orientation
For the third test the same previously used materials for the wall and glazing were put to the test with different
orientations of the façade. The given inputs to the tool were the same as the former simulation; only the other
three orientations were added to the orientation input.
Figure 7. Best glazing material based on energy consumption for different WWRs
Location: Cairo, Egypt
Figure 7 shows how the proper material for each WWR varies in different orientations which is due to the change
of the amount of direct solar radiation on the façade, which reduces the effectiveness of photovoltaics on WWR
optimal value and the amount of heat gain from the transparent part of the façade which would make higher WWR’s
and more transparent materials suitable to a greater extent as they would lead to lower energy need for artificial
lighting. Figure 8 clarifies the fluctuation of different sections of energy in different orientations while it also
illustrates how the energy consumption of the same office cell could change up to %44 which would be1913 kWh
per year only by a change in the cardinal direction which it is oriented.
Figure 8. Different sections of the total load at the optimized solutions for each orientation
(East=19% - South=12% - West=12% - North=30%) in Cairo, Egypt
0
10
20
30
40
50
60
70
80
90
100
North East South West
WWR(%)
NormalGlass BIPV1 BIPV2 BIPV3
‐3100
‐2100
‐1100
‐100
900
1900
2900
3900
4900
East South West North
AnnualLoads(KWh/year)
Cooling Heating Lighting PV Total
3.4. Geographical Location
For the last test the same simulations were done for all the selected location inputs. Cities of Palermo and
Marseille were also added to Cairo to study the location input’s impact on the façade solution. BIPV 1 was
selected as it had the highest capability of reducing the energy loads in the previous tests.
Figure 9. The optimal WWRs Figure 10. The total energy loads at the optimal WWRs
Figure 9 visualizes how the ideal amount of WWR varies by the change of geographical location. Marseille has
the highest recommended sizes for windows and from Figure 10 it could be seen that it also has the uppermost
energy consumption among the selected cities due to its heating dominated climate. Cairo on the other hand is
the most cooling dominated climate which resulted in lower WWRs as optimum. The changes were small in east
and south orientations and the best result would be derived from almost the same WWRs as in Palermo. In general
west and north orientations had the most contrast between the three locations, while east and south had the least.
4. Conclusion
The test results demonstrated how a change in each of the material properties, like the efficiency of the
photovoltaics, visible transmittance (VT), and solar heat gain coefficient (SHGC) of windows, window-to-wall ratio,
the orientation of the facade and the location of the building could result in a completely different ideal solution.
They clarified that not only BIPV glazing materials would not be beneficial at all times, but also they could
considerably increase the total energy consumption in some cases. This underlines the decision makers’ need for
a precise set of data from computational simulations to allow them to choose the best active and passive design
options and avoid making energy-wise costly mistakes. It also helps them to make their decisions based on their
own pre-defined requirements, if they need to meet some regulations for daylighting or a demand for having larger
windows to comply with visual needs of the occupants the proper solution would be different. It is also worth
mentioning that the illustrated results are based on the specifications of today’s existing building materials on the
market. With the developments on material properties every day like the efficiency of BIPV or U-value of glazing
or walls, the ideal solution would be different through the time due to these ever-changing values. By taking into
consideration the valuable data from computational simulations, designers could catch up with these
developments and have the highest performance designs possible with the latest materials.
0
10
20
30
40
50
60
North
East
South
West
Cairo Palermo Marseille
0
1000
2000
3000
4000
5000
6000
North
East
South
West
Cairo Palermo Marseille
REFERENCES
[1] Yezioro, A; Dong, B; Leite, F. 2008. An applied artificial intelligence approach towards assessing building
performance simulation tools. Energy and Buildings 40(4):612-620.
[2] Lobaccaro G; Fiorito F; Masera G; Prasad D. 2012. Urban solar district: a case study of geometric
optimization of solar facades for a residential building in Milan. International Conference “Solar 2012”.
Melbourne. December 2012
[3] Lovati, M. 2014. A BiPV design optimization method. International Conference SOLARTR 2014. Izmir.
November 2014.
[4] Robinson, L; Athienitis, A. 2009. Design methodology for optimization of electricity generation and daylight
utilization for façade with semitransparent photovoltaics. Proceedings of Building Simulation 2009: 11th
International IBPSA Conference Glasgow, Scotland. pp.811-818
[5] Sandra, M; Frontini, F; Wienold, J. 2011. Comfort and building performance analysis of transparent building
integrated silicon photovoltaics. Proceedings of Building Simulation 2011: 12th Conference of International
Building Performance Simulation Association, Sydney. pp.2080-2087
[6] Lahmidi, H; Pernodet, F; Marchio, D; Filfli, S; Roujol, S. 2016. Optimization method using genetic algorithms
for designing high performance building.
[7] Kim, S; A.Zadeh, P; Staub-French, Sh; Froese, Th; Cavka, B. 2016. Assessment of the Impact of Window
Size, Position and Orientation on Building Energy Load Using BIM. Procedia Engineering, 145:1424-1431.
[8] EN 12464-1. Light and lighting - Lighting of work places. Part 1: Indoor work places.
[9] EnergyPlus engineering reference,
https://energyplus.net/sites/default/files/pdfs_v8.3.0/EngineeringReference.pdf
[10] RADIANCE, the Radiance 4.0 Synthetic Imaging System, Lawrence Berkeley, National Laboratory, 2010.
[11] Geoffrey G. Roy. 2000. A Comparative Study of Lighting Simulation Packages Suitable for use in
Architectural Design. Australia. Murdoch University.
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