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Procedia Engineering 145 ( 2016 ) 1424 – 1431
1877-7058 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of ICSDEC 2016
doi: 10.1016/j.proeng.2016.04.179
ScienceDirect
Available online at www.sciencedirect.com
International Conference on Sustainable Design, Engineering and Construction
Assessment of the Impact of Window Size, Position and Orientation
on Building Energy Load Using BIM
Soojung Kima,*, Puyan A. Zadehb, Sheryl Staub-Frenchc, Thomas Froesed, Belgin Terim
Cavkae
aMASc Candidate, Department of Civil Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
bPh.D., Postdoctoral Fellow, Department of Civil Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada
c Ph.D., Assoc. Prof., Department of Civil Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada
d Ph.D., Prof., Department of Civil Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada
ePh.D., Postdoctoral Fellow, Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
Abstract
In improving energy efficiency of buildings, windows play a significant role as they largely influence the energy load. Although
there are many studies about the energy efficient window design, a rigorous study is missing which analyzes the mutual impact of
windows’ size, position and orientation on the energy load. This study aims to address this gap through a case study on a single
family house. For this aim, 65 different design scenarios are created which vary by window size, position and orientation.
Building information models (BIMs) are created for each scenario via Autodesk Revit®, and are used for the calculation of the
total energy load conducted by Autodesk Green Building Studio®. In the first analysis stage, window-to-wall ratio (WWR) and
the windows’ position are studied to assess their effect on the energy load. The preliminary results at this stage indicate that the
total energy load increases when the WWR grows, and the windows’ position has the biggest impact on the load when the WWR
is 20. Using these results, in the next stage, the position of windows in different orientation is studied to assess how the energy
load changes by windows’ position in each orientation. The results show that the building requires the lowest load when the
windows are located in the middle height in all orientations, and the east windows’ positioning affects the total energy load the
most.
Keywords: Building energy load; Window design; Energy simulation; Building information modeling
* Soojung Kim. Tel.: +1-778-227-9570.
E-mail address: soojung.kim@alumni.ubc.ca
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1. Introduction
Buildings are accounting for more than 40% of global primary energy use, produce substantially more carbon
emissions than those in the transportation sector, and so they are the largest energy consuming sector in the world
[1]. As the concern about the environmental impacts of building is increasing, private and public organizations are
progressively requiring the building industry to design and construct buildings with minimal environmental impact
[2]. Consequently, many studies have been done regarding the energy efficient building design. In this regard,
windows are responsible for more than 10% of the building energy load and so are revealed to have considerable
influence on the total energy consumption [3]. Several studies were conducted about the impact of window design
on energy load regarding various factors of windows, to reduce the energy waste by windows [7,8,11]. However, as
the geometry and systems of buildings are getting complicated, more complex studies are needed which analyze the
mutual effect of different design factors [4]. This paper, therefore, aims to address this gap by assessing the influence
of window design on building heating and cooling load, focusing on the windows’ size, position and orientation at
the same time. For this aim, we explain how to prepare the required building information models (BIMs), and create
the required scenarios in order to perform rigorous energy load analyses. The entire simulation process is divided
into two stages. In the first stage, we show how the window size and position affect the building energy load by
creating models of 29 scenarios. In the following stage, the impact of window position and orientation is analyzed
through 36 different scenarios. The case study in this research is conducted on a single family house, which is a two-
story, 1620ft² building located in Vancouver, Canada. This building is built on the campus of University of British
Columbia as a pilot home of AYO Smart Home company. This pilot project aims to develop sustainable, energy
efficient and affordable housing for remote communities.
1.1. Impact of window on building energy load
As one of the key approaches to low energy design is to invest in the building’s form and enclosure (e.g.,
windows, walls) [5], many studies treat the influence of enclosures on the energy load. Especially, several studies
have been done on the effect of window design on building energy load regarding the factors such as window size,
position, glazing properties and orientation. In early studies, one or two factors were analyzed concurrently. The
impact of window size was analyzed solely [6], and several studies were done on window size and position [7,8,9].
Glazing properties and size were also considered together [10], and the orientation and size were analyzed at the
same time [11]. In addition, few precedent articles considered the effect of orientation, size and glazing properties
[12,13,14]. However, the research on the influence of window size, position and orientation on energy load is still
missing. As the building designs are getting more dynamic and complicated, more detailed and thorough analysis on
various window design factors should be conducted. Therefore, the assessment of the impact of the three factors on
building energy load is studied in this paper.
1.2. Building information modeling for energy analysis
According to the recent studies, BIM based energy analysis is considered to be useful for energy assessment of
building. Fundamentally, utilizing BIM as a data source for energy analysis makes the data input more efficient and
the existing data more reusable [15]. In addition, as BIM allows a 'live', parametrically controlled digital model to be
connected with a simulation program, it is fairly simple for designers to use and conduct the performance
evaluations within a software interface they are already familiar with [16]. The best benefit is that the results of the
energy analysis can be viewed immediately and changes to design can be immediately incorporated, thus more
design iterations can be evaluated to improve efficiency and meet sustainability goals [17]. One of the studies
proposed to utilize BIM for evaluating building energy performance by showing its efficiency, based on a case study
that used BIM technology for optimizing energy-efficient design, which showed positive result [18].
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2. Research objective and methodology
The objective of this research is to assess the impact of window design on building energy load. Since the
building energy load is affected by a combination of various design factors, the three factors of the window, its size,
position, and orientation are considered concurrently by creating various combination scenarios. The result is
expected to give a guide to the designers, by showing how the window factors influence the building energy load.
In this paper, window size means window-to-wall ratio, which is the measure of the percentage area determined
by dividing the building's glazed area by its wall area. The window position means the height of the window from
the floor, and it is divided into High, Middle, and Low position. High position is when the top of the window is
aligned with the top of the wall, the Middle position indicates the midpoint of the window is positioned in the
middle of the wall, and the Low position means the bottom of the window is placed at the bottom of the wall. Lastly,
window orientation means the facing directions of the windows, which are north, east, south, and west, in this case.
The total load in the research means the annual heating load and cooling load. The lighting load has fixed value, as
the project team indicated the lighting load in their design.
The methodology of the research is shown in Fig. 1. Based on the initial BIM model provided, the geometry of
the model is simplified by eliminating the shadings and exterior structures to analyze the impact of window
exclusively. Properties related to energy load are set and maintained throughout the whole research to eliminate the
effect of the factors other than windows. Then energy analysis is conducted in two parts with total 65 scenarios. In
the first part, window size and position is changed by 29 scenarios as Fig. 2 shows. Energy simulation is conducted
for each scenario and the results are analyzed. The results indicate how the window size affects the energy load and
which size of the window has the most influence on the load when its position is changed. On the basis of the results
of the first part, the window position and the orientation are modified by total 36 scenarios, 9 scenarios each on
north, east, south and west facing windows. Fig. 3 shows the north facing windows scenarios. The north facing
window position is changing while the windows on the other sides are in the same position. This analysis is
expected to reveal what combination of window position requires lowest energy load, and which side of window’s
position has the biggest impact on the energy load. BIM model is created and modified by using Revit®. Energy
simulation is conducted by Green Building Studio®, which is connected to Revit®.
Fig. 1. Research methodology
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Soojung Kim et al. / Procedia Engineering 145 ( 2016 ) 1424 – 1431
Fig. 2. Window size and position changing scenarios
Fig. 3. Window position and orientation changing scenarios (North facing windows)
3. Research activities
3.1. Base model
To conduct the energy analysis, a simplified BIM model is created, based on a BIM model of a single family
house. It is a two-storied house, which has two rooms and one bathroom on each floor, and one living room that is
connected throughout two floors. Every room has windows, except an unconditioned storage room on the first floor,
which has no window. As Fig. 4 (a) shows, the properties of rooms, such as occupancy schedules, lighting load, and
equipment load are fixed in every scenario. The exterior structures, shadings, and doors are eliminated to observe
the impact of windows exclusively. Fig. 4 (b) is the original BIM model of the building, and (c) is the simplified
BIM model used for the energy analysis in the research.
Fig. 4. (a) Properties setting; (b) Original BIM model; (c) Simplified BIM model
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3.2. Energy analysis process and results
3.2.1. Impact of window size and position on energy load
To assess the impact of window size and position on building energy load, window size and position are changed
in 29 scenarios. Window-to-wall ratio (WWR) is changed from 0 to 100 percentages by 10 percentage scale and the
position of the window is changed from high, middle to low. Fig. 5 shows the snapshots of BIM model, which are
10 percentages and 20 percentages scenarios. As it shows, the window position is changing from high, middle, and
low height. There is one window on the first floor since the storage room on the first floor has no window in the
original design, and there are two separate windows on the second floor because there is an unconditioned space
between two rooms. Energy simulation is conducted after each scenario model is made and the results are collected.
Fig. 5. Snapshots of window size and position changing scenarios (North facing windows)
Fig. 6 shows that the annual energy load increases as the window size gets bigger, regardless of the window
position. The lowest total energy load is 2923kWh when WWR is 0, and the biggest load is 4250kWh when WWR
is 100. The gap between the two loads is 1330kWh, which is 45 percentages of the lowest, indicating that window
size largely impacts the energy load of the building. Therefore, designers should carefully consider the impact of the
window size, not simply increasing the size to achieve view and daylight. Fig. 7 shows the energy load variation by
window position change in each window size. The difference indicates the degree of impact of window position on
energy load. The maximum difference is 23kWh, when the window-to-wall ratio is 20 percentages. Therefore,
although the influence is insignificant, the position of the window has the biggest influence on energy load when
WWR is 20 percentages. As the other window sizes show less or no load variation, it can be assumed that window
position merely affects the energy load when WWR is other than 20 percentages.
Fig. 6. Annual energy load by window size and position change
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Fig. 7. Energy load variation by window position change in each size
3.2.2. Impact of window position and orientation on energy load
In the second part, the impact of window position and orientation is assessed by new scenarios. The WWR is
fixed to 20, as the previous part showed that the window position has the biggest influence when WWR is 20. The
other window size scenarios are assumed to have small or no influence on the energy load. To estimate the impact of
window position each building face, the scenarios are created as Fig. 8. The position of north facing window is
changed from high to low, while the other side of the windows is in the same position. Same scenarios are created in
the other three orientations. These scenarios are analyzed to find out which combination of window position requires
the lowest energy load, and which side of window’s position has the biggest impact on the energy load.
Fig. 8. Snapshots of window position and orientation changing scenarios (North facing windows)
The results are classified by orientation, as Fig. 9. Scenario name indicates how the windows are positioned. For
example, ‘High-North-Middle’ means windows are in high position except north facing window positioned at the
middle. The lowest load is 3116.49kWh in the ‘Middle’, when all the windows are located in middle height and the
highest load is 3146.965kWh in the ‘Middle-East-Low’, when the east window is positioned at low and others are
located at the middle. Fig. 10 shows the energy load variation by window position in each orientation. The biggest
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load variation is 1% in the east facing window scenarios, meaning that the east side window position has the biggest
impact on the energy load. The other side windows show less or no load variation.
Fig. 9. Annual energy load by window position and orientation change
Fig. 10. Energy load variation by window position change in each orientation
4. Conclusion
To analyze the impact of window design on building energy load, the window size, position, and orientation are
changed in 65 scenarios, and the heating and cooling load of each scenario is analyzed. First, the size and position of
the windows are changed in 29 scenarios. The energy simulation result shows that the annual energy load
significantly increases as the window size increases regardless of the window position. This indicates that the
window size is the critical factor that should be considered during window design phase. In addition, the load
variation by the window position in each size indicates that the position of the window has the biggest influence on
energy load when WWR is 20. In this case, the variation has insignificant impact as it is less than 30kWh, but the
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variation is expected to increase in bigger building case. In the next stage, the window position of each orientation
of the building is changed in 36 scenarios. The energy load is the lowest when all the windows are located in middle
height, and the load variation by window position shows that the east window has the biggest influence on total
energy load. The variation is 1% of the total energy, which is 30kWh in this building, but it will increase when it is
applied to bigger scaled building. Therefore, the east window position should be designed by considering the impact
on energy load.
5. Limitation and further research
As this study is based on a small-scale building, the total energy load is relatively small, so the energy load
variation is insignificant when it is interpreted to cost. If the same methodology is applied to a bigger scale building,
the energy load variation would be higher and this might lead to considerable cost saving. Therefore, further
research should be done to assess of the impact of the window design on the energy load of bigger building. Also
this research is limited to a case study of a building located in Vancouver, BC. If the location and sun angle changes,
the critical orientation of window might change. More studies in various location should be conducted. In addition,
visual comfort is neglected in this research to consider the heating and cooling load exclusively. In real project,
visual comfort should be considered when the window is designed. Lighting load is also not considered since the
required lighting load was relatively small in the original design, but if the building scale changes and users require
more lighting, the lighting load should be considered.
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