SHB2011 - 5th International Symposium on Sustainable Healthy Buildings, Seoul, Korea
10 February 2011
Prediction of discomfort glare from windows:
Influence of the subjective evaluation of window views
Jeong Tai Kim1, Ju Young Shin1, Geun Young Yun1*
1
Department of Architectural Engineering, Kyung Hee University, Yongin 446-701, Korea
* Corresponding Author: GY Yun (gyyun@khu.ac.kr)
Abstract
Lighting in buildings exerts a large influence on the comfort, well-being, and health of
occupants. A window plays a major role in providing both natural lighting in buildings and a
view out, so that occupants can keep in touch with the external world. Thus, it is essential to
understand what are visual requirements to meet occupants’ needs and how window views
affect those requirements. This study aims to reveal occupant perception on window views
and to identify its effects on the assessment of glare from windows. This study presents the
results from experiment conducted in a chamber with a simulated window, which can render
various windows views and luminance conditions. Forty eight subjects participated in the
experiment. The results show the evidence that the glare sensation of occupants change with
the occupant perception on window views under the same luminous conditions. The paper
develops a discomfort glare index that predicts a subjective discomfort glare as a function of
background and window luminances, position index, and perception on window views.
Comparison between he developed discomfort glare index and existing glare prediction
models has been discussed.
Keywords: Discomfort glare; simulated window; view; visual discomfort; perception; glare
index
Introduction
Windows have significant implications for buildings and their occupants. The window plays
a major role in controlling the amount of daylight transfer between the buildings and the
environment. The use of daylight is of importance, as daylighting would potentially save
considerable amount of lighting energy use [1,2]. The window has also the psychological
benefits by providing a view out. Occupants’ need to communicate with the outside and the
psychological and physiological impacts of daylight on occupants make the window an
essential element in buildings.
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Discomfort glare that is caused by daylight from windows is common problem in buildings
and more importantly this becomes a potentially barrier to the active use of daylight by
occupants. Existing models for the prediction of discomfort glare evaluate visual
environments as a function of glare source and background luminances, and the relative
position and distance of observers from the glare source. However recent studies show that
the subjective evaluation of discomfort glare is also influenced by window views [3, 4].
This study aims to reveal occupant perception window views by carrying out the factor
analysis of experiment data and to identify its effects on the subjective evaluation of
discomfort glare windows.
Experimental methods
Simulated windows and window views
A simulated window that has been used in the research of discomfort glare [1,2,3,5] has been
applied in this study. [1,2,3,5]. The simulated window can render the luminance of windows
ranging from 0 to 15,000 cd/m2 and can display various window scenes such as natural and
man-made scenes with near and distant views.
Table 1 shows ten window views chosen for this study. The views are divided into distant
and near views. The distant view has a three-layer image with the sky and the near view
consists of a two-layer image without the sky. The contents of examined views include
natural and man-made scenes commonly found in Korea. Average window luminance’s
range from 1000 cd /m2 to 10000 cd/m2 with an equal interval of 0.5 log luminance units.
Table 1. The images of the simulated view
Natural
Natural
Man made
land
river
Mixed land
Mixed river
distant
Near
Evaluation of discomfort glare and subjective impression
The discomfort comfort scale proposed by Hopkinson [6] was adopted in this study [Fig. 1].
Questions to evaluate discomfort glare include short descriptions of the meaning of
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10 February 2011
discomfort scales in both English and Korean in order to prevent the misunderstanding of the
scales. For the evaluation of subjective impression on window views, semantic scales
proposed by Spencer et al. [7] have been used in this study.
Fig. 1. Discomfort glare evaluation scale
Experimental procedure
Experiment was carried out in a laboratory space and a subject took a seat on a chair at 1.5 m
away from the simulated window [Fig.2]. Once an experimenter explained the experiment
procedure and the definition of the discomfort glare scale, the subject adapted their eyes to
the lighting conditions of the laboratory for two minutes. The subject completed first the
questions related to the subjective impression of given window views. The experimenter then
set up the luminance condition. The subject saw the centre of the window for about five
seconds and evaluated discomfort glare. The evaluation of discomfort glare was repeated for
five luminance conditions of each view. Window views and mean luminance were randomly
allocated and Fig. 3 shows the experiment procedure.
Fig. 2. Experimental layout
Fig. 3. The procedure of the experiment
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SHB2011 - 5th International Symposium on Sustainable Healthy Buildings, Seoul, Korea
10 February 2011
The subjects
The number of participants was 48 (24 male, 24 female). The subjects were recruited from
University students and their age ranged from 20 to 32. The data were analyzed without
discrimination of the gender, age, eyesight and eye conditions. A financial incentive of 21 US
dollar was paid to a subject for participating in the experiment.
Results
Factor analysis
Table 2 summarises the results from the factor analysis on experimental data. The value in
Table 2 is a factor loading which is a correlation coefficient between independent variables
(i.e. subjective impression) and compoents. An independent variable is reasonably
represented by a selected factor when a factor loading is over 0.4 [8]. Six factors with an
eigenvalue of 1 or higher shown in Table 2.
Figure 4 is a scree plot that shows the eigenvalues of components. The eigenvalue of a
component is a measure that indicates a variance of all independent variables, which is
explained by the component. The eigenvalue varies little with a component number of five or
higher. This implies that a component number higher than four in this case can be considered
as redundant with components with higher eigenvalues. Thus, four components were selected
to represent 27 variables that evaluate subjective impression on window views.
Figure 4 Scree plot that shows changes in eigenvalues as a function of component number
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10 February 2011
Table 2 Correlation coefficients between subjective impression and components obtained
from factor analysis
Component
Subjective impression
1
2
3
4
5
6
Spacious - Cramped
.858
-.066
.060
.255
.017
-.040
Beautiful - Ugly
.857
.011
.030
.110
.113
.116
Like - Dislike
.852
-.076
.065
.164
.077
.050
Satisfying – Frustrating
.845
-.063
.081
.160
.159
.150
Pleasant - Unpleasant
.843
-.074
.134
.097
.127
.070
Relaxed – Tense
.826
.067
.009
.065
.035
.020
Harmony – Discord
.798
.114
.046
.064
.093
.050
Cheerful – Somber
.779
-.167
.274
-.033
.146
.005
Wide - Narrow
.732
.004
-.040
.464
.037
-.022
Horizontal – Vertical
.728
.129
-.191
.167
-.058
-.111
Faces Clear – Faces
.689
-.070
.103
.493
-.017
-.002
Obscure
Warm – Cool
.655
-.157
.249
-.347
.006
-.114
Stable – Unstable
.601
.358
.114
.000
.227
.253
Long – Short
.561
-.080
-.099
.555
-.009
-.050
Simple – Complex
.178
.751
-.072
-.066
.134
-.032
Interesting –
.366
-.736
.070
.071
.207
-.009
Monotonous
Uniform – Non
.053
.639
.045
-.003
.460
.069
Uniform
Colorful – Colorless
.502
-.613
.195
-.100
.254
-.074
Uncluttered – Cluttered
.354
.533
-.006
.196
.502
.011
Clear – Hazy
.029
-.036
.915
.002
.014
-.012
Distinct – Vague
-.008
.047
.873
.047
.143
.093
Bright – Dim
.395
-.263
.678
.080
.019
-.041
Large – Small
.375
.102
-.054
.641
.014
.206
Specular – Non.048
-.072
.208
.548
.200
-.155
Specular
Focused – Unfocused
.075
.156
.036
-.058
.798
-.038
Public – Private
.054
-.063
.080
.139
.467
.078
Real – Fantasy
.098
.033
.041
-.023
.067
.941
Discomfort glare prediction model
This study develops a discomfort glare prediction model, which considers subjective
impression on a window view when evaluating discomfort glare from a window. The model
uses a Hopkinson’s Discomfort Glare Index [ ] and the formula of the model is as follows;
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10 February 2011
Modified DGI = aDGI bComponent1 cComonent2 d Component3 eComponent4 f
Where DGI is the Hopkinson’s discomfort glare index and Components 1, 2,3, and 4 are the
subjective impression factors selected in the factor analysis.
A multiple regression result, which shows regression coefficient values, is shown in Table 3.
The Analysis of Variance (ANOVA) result indicates that the overall regression model is
statistically significant (F(5,2394), F=993.387, P < 0.001) and also t-tests results confirm that
independent variables are significant (P < 0.001 for all variables).
Table 3. Regression result
(Constant)
Component 1
Component 2
Component 3
Component 4
DGI
Unstandardized
Coefficients
B
Std. Error
-26.384
.680
-.228
.049
.182
.049
.224
.049
-.140
.049
1.392
.020
Standardized
Coefficients
Beta
-.054
.043
.053
-.033
.817
t
-38.809
-4.661
3.730
4.577
-2.861
70.082
Sig.
.000
.000
.000
.000
.004
.000
Discussion and Conclusions
The experimental results provide evidence that the glare sensation of occupants change with
their subjective impression on the window views under the same luminous conditions. The
results also indicate that the factor analysis is useful to reveal the subjective impression on
the window views.
This study has developed a prediction model to evaluate discomfort glare as a function of
background and window lumiances, position index, minimum, maximum and mean window
lumiances, and subjective impression on window views. Comparison between the developed
prediction model and experimental results show a good agreement.
Acknowledgements
This research was supported by Basic Science Research Program through the National
Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and
Technology (No. 2010-0001860).
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