Lecture 13: Visual Comfort & Occupant Behavior
Transcript of Lecture 13: Visual Comfort & Occupant Behavior
MIT 4.430 Daylighting, Instructor C Reinhart 1
Massachusetts Institute of Technology Department of Architecture Building Technology Program
Christoph Reinhart4.430 Visual Comfort & Occupant BehaviorChristoph Reinhart 4.430 Visual Comfort Occupant Behavior
4.430 Daylighting
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Daylighting/Lighting in LEED
Indoor Environmental Quality section:credits for:
(a) glazing factor (daylight factor) of 2% (b) Between 250 and 5000 lux under equinox
clear sky at 9AM and 3PM.
view to the outside
Compliance via spreadsheet method.
MIT 4.430 Daylighting, Instructor C Reinhart 3
this formula?
How many of you have used
LEED 2.2 Glazing Factor Formula
External obstructions are not considered.
Credit 8.2 Views for 90% of Spaces Achieve direct line of sight to vision glazing for building occupants in 90% of all regularly occupied spaces. Examples for exceptions copy rooms, storage areas, mechanical, laundry and other low occupancy support areas.
View to the Outside in LEED I 2
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View to the Outside in LEED II
View to the Outside
• Size and content matter • Information rich views with natural
elements provide satisfaction and health benefits
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Image by MIT OpenCourseWare.
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Daylighting/Lighting in LEED
Sustainable Sites: Light Pollution Credit “eliminate light trespass from the building and site, improve night sky access and reduce development impact on nocturnal environments”.
Occupant Behavior
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blinds always down, slats at 45 o
blinds always up
USER BEHAVIOUR ?!
Occupant Behavior
dayl
ight
aut
onom
y [%
]
100
80
60
40
20
0 blinds always fully closed
0.5 1 1.5 2 2.5 3.3 4.5 5.5 6.5 7.5 distance to facade [m]
architecture: Meier-Weinbrenner-Single, Nürtingenarchitecture: Meier-Weinbrenner-Single, Nürtingen
Monitoring User Behavior
Paper: Reinhart C F, Voss K, Monitoring manual control of electric lighting and blinds. Lighting Research & Technology, 35:3 pp. 243-260, 2003.
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HOBO data logger
Illuminance Temperature
occupancy
Monitoring Setup in the Offices
receiver 2414.5 MHz
data acquisition EIB system Blind setting
video surveillance camera
Monitoring Blind Usage
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Example Picture
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0
0.25
0.5
0.75
1
0 100 200 300 400 500 600minimum work plane illuminance [lux]
switc
h-on
pro
babi
lity
at a
rriv
al
Switch-On Probability (I)
1
switc
h-on
pro
babi
lity
at a
rriv
al0.75
0.5
type 1
type 2
0.25
0 0 100 200 300 400 500 600
minimum work plane illuminance [lux]
JimJim LoLovvee,, UnivUniversitersityy ofof CalgaryCalgary
Switch-On Probability (II) 1
switc
h-on
pro
babi
lity
at a
rriv
al
0.75
0.5
Hunt 1978 Lamparter 2000
0.25
0
0 100 200 300 400 500 600 minimum work plane illuminance [lux]
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People are Consistent but Different1
larr
iv 0.8
at a
bilit
y
0.6
h-on
pro
ba
0.4
itc 0.2
ws
00 100 200 300 400 500
minimum work plane illuminance [lux]
0
0.25
0.5
0.75
1
switc
h-of
f pro
babi
lity
whe
n le
avin
g
<30 minutes30-59 minutes
1-2 hours2-4 hours
4-12 hours12-24 hours
24+ hours
Pigg et al. University of WisconsinPigg et al. University of Wisconsin
0
0.25
0.5
0.75
1
switc
h-of
f pro
babi
lity
whe
n le
avin
g no controls
<30 minutes30-59 minutes
1-2 hours2-4 hours
4-12 hours12-24 hours
24+ hours0
0.25
0.5
0.75
1
switc
h-of
f pro
babi
lity
whe
n le
avin
g no controls
occupancy sensor
<30 minutes30-59 minutes
1-2 hours2-4 hours
4-12 hours12-24 hours
24+ hours
behavioral patterns change in the presence of automated controlsbehavioral patterns change in the presence of automated controls
0
0.25
0.5
0.75
1
switc
h-of
f pro
babi
lity
whe
n le
avin
g
no controlsdimmed system
Switch-Off Probability
occupancy sensor
<30 minutes 1-2 hours 4-12 hours 24+ hours30-59 minutes 2-4 hours 12-24 hours
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Intermediate Switch-On Probability
0
0.01
0.02
0.03
0 100 200 300 400 500 600 700 minimum work plane illuminance [lux]
inte
rmed
iate
sw
itch-
on p
roba
bilit
y
Manual Lighting Control Algorithm 100
80
blin
ds o
cclu
sion
[%]
60
40
20
0
Inoue
Lamparter
0 1 2 3 solar penetration depth [m]
Blinds get lowered to avoid direct sunlight falling on the work plane.
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stochastic process:switch on probability
(F g. 7-5)
NO
NO
Has the room been deser ed or onger than sensor
Is there an occupancy sensor?
YES
Does occupan eav
NO
stochastic procesntermediate sw tch
probabi ty (F g. 7 7)
Does occupant arr ve?
NO
annual occupancy profiles
annual illuminance profiles
Lightswitch 2002
Lightswitch Algorithm (stochastic)
el. lighting/blinds profile
Model Overview
Paper: Reinhart C F, Lightswitch 2002: A model for manual control of electric lighting and blinds", Solar Energy, 77:1 pp. 15-28, 2004
Switch lights on
Is work place already occupied?
YES
i
NO
YES
YES
t f l delay time?
Are lights switched on?
Does occupant arrive?
YES
Switch lights off
NO YES
Does occupant leave?
Are lights switched on? NO
YES
stochastic process: Pigg’s switch off probability with or without occupancy
sensor (Fig. 7-5)
NONO
NO
Switch lights off
YES
YES
t l e? YES
s: i i on
li i -
NO YES
Switch lights on
i YES
NO
0
0.2
0.4
0.6
0.8
1
0 100 200 300 400 500 600 minimum work plane illumiance [lux]
switc
h-on
pro
babi
lity
Hunt
Lamparter
stochastic process: switch on probability
Lightswitch - Manual Lighting Control
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Lightswitch - Manual Blind Control
Lightswitch - Manual Blind Control
Define work plane sensors that define where the occupants are usually located.
Associate sensors with shading groups. A shading group consists of a (set of) blinds that are opened and lowered at the same time.
Check when direct sunlight (>50Wm-2) is incident on a work plan sensor.
Close shading device if yes until occupant is away for more than an hour.
work plane sensors
window with blinds
) ) )
) ) )
) )
) ) )
)
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Blind Use in New York City Classrooms
183 teacher surveys, 9 participating schools
MDesS thesis, Jennifer Sze 2009 Question 14: How often do you adjust the shading device(s)?
DIVA Demo: Occupant Behavior
5%8%
31%
21%
18%
17%
Multiple times a dayOnce or twice a weekOnce or twice a month
NeverOtherN/A
Image by MIT OpenCourseWare.
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Modeling Occupant Behavior
No BlindsNo Blinds With BlindsWith Blinds
Detecting Glare
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–
What is glare? Glare is a subjective human sensation that describes ‘light within the field of vision that is brighter than the brightness to which the eyes are adapted’ (HarperCollins 2002).
Illustration by ZStardust on Wikimedia Commons.
Glare Indices
n
i
ss
b P L
LUGR
1 2
2
10 25.0
log8
CIE Unified Glare Rating
A glare index is a numerical evaluation of high dynamic range images using a mathematical formula that has been derived from human subject studies.
Example indices include the unified glare rating (UGR) and the daylight glare index (DGI). All of these equations were derived from experiments with artificial glare sources none of them under real daylight conditions.
The reason for this is that until recently it has been next to impossible to collect high dynamic range images of daylit scenes under continuously changing lighting levels.
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Weak correlation between DGI and CGI (cventional galre metrics) and occupant evaluations.
Courtesy of Elsevier. Used with permission.
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Daylight Glare Probability (DGP)
DGP is a recently proposed discomfort glare index that was derived byWienold and Christoffersen from laboratory studies in daylit spaces using 72test subjects in Denmark and Germany.
Two identical, side-by-side test rooms were used. In Room 1 a CCD camerabased luminance mapping technology was installed at the exact position andorientation as the head of the human subject in Room 2.
Paper: Wienold & Christoffersen, " Evaluation methods and development of a new glare prediction model for daylightenvironments with the use of CCD cameras ", Energy & Buildings 2006.
Room 1: CCD CameraRoom 1: CCD Camera Room 2: Human SubjectRoom 2: Human Subject
Image-based Glare Source Detection using the Radiance evalglare Program
Paper: Wienold & Christoffersen, " Evaluation methods and development of a new glare prediction model for daylightenvironments with the use of CCD cameras ", Energy & Buildings 2006.
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In Search of a Glare Metric…
Paper: Wienold & Christoffersen, Evaluation methods and development of a new glare prediction model for daylight environments with the use of CCD cameras ", Energy & Buildings 2006.
Weak correlation between DGI and CGI (conventional glare metrics) and occupant evaluations.
Courtesy of Elsevier. Used with permission.
In Search of a Glare Metric…
Vertical eye illuminance promising for the first term.
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Image by MIT OpenCourseWare.
R2=0.12
Window Luminance [cd/m2]
Window Luminance+- Standard deviation of DGP
DGI+-
+-+-
Standard deviation
0 2000 4000 6000 80000%
20%
40%
60%
80%
100%
Perc
enta
ge o
f di
stur
bed
pers
ons
Perc
enta
ge o
f di
stur
bed
pers
ons
Perc
enta
ge o
f di
stur
bed
pers
ons
Perc
enta
ge o
f di
stur
bed
pers
ons
R2=0.56
CGI
CGI
Standard deviation
0 10 20 30 40 500%
20%
40%
60%
80%
100%
Daylight glare index DGI
R2=0.56
0 10 20 30 40 50 600%
20%
40%
60%
80%
100%
Vertical Eye Illuminance [lux]
Vertical Eye Illuminance
R2=0.77
0 2000 4000 6000 8000 100000%
20%
40%
60%
80%
100%
Standard deviation
Total responses: 349Number of responses per
illuminance- class:29
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""
Final Daylight Glare Probability Metric
Paper: Wienold & Christoffersen, Evaluation methods and development of a new glare prediction model for daylight environments with the use of CCD cameras , Energy & Buildings 2006.
DGP allows users to go back and forth between simulation and reality through HDR photography
HDR Image (Digital Camera) HDR Image (Radiance)
evalglare program
instantaneous daylight glare probability
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0%0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
R2 = 0.94
DGP+_
+_ Standard deviation of DGP
Standard deviation of binomial deviation
Total responses: 349Number of responses per DGP - class: 29
Daylight glare probability DGP
Pro
bab
ility
of
dis
turb
ed
per
son
s
Image by MIT OpenCourseWare.
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DGP Comfort Ranges
DGP < 35% imperceptible
35% < DPP < 40% perceptible
40% <DGP < 45% disturbing
DGP > 45% intolerable
Example DGP Calculation
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DIVA Demo: DGP Point in Time Calculation
Towards Annual Glare Calculations
Wienold developed a process through which the annual glare calculation is split into a regular Daysim illuminance calculation and an ab=0 contrast image.
Paper: J Wienold, Dynamic Daylight Glare Evaluation , Building Simulation 2009, Glasgow Scotland.
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Generated visualizations for illuminance calculation
removed due to copyright restrictions.
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Comparison of Simplified DGP and hour -by-hour method
Paper: J Wienold, Dynamic Daylight Glare Evaluation , Building Simulation 2009, Glasgow Scotland, 2009.
Annual Glare Map
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Graph of vertical illuminance removed due to copyright restrictions.
DGP Comfort Ranges
Paper: C F Reinhart, Simulation-based Daylight Performance Predictions", Book chapter in Building Performance Simulation for Design and Operation, Editors J Hensen and R Lamberts, Taylor & Francis, 2011.
Expanded Blind Control Model: Close Blinds when DGP > 40%
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Imperceptible
PerceptibleDisturbingIntolerable
Daylight glare probability [%]20 25 30 35 40 45 50 55 60
1500
1200
900
600
300
0Num
ber
of w
orki
ng h
ours
with
DG
P ab
ove
No blinds
Blinds always lowered
Blinds always lowered
100
80
60
40
20
00 1 2 3 4 5 6 7
Day
light
Aut
onom
y [%
]
Distance to facade [m]
No blinds
Active blind use (avoid direct sunlight)
Active blind use (avoid direct sunlight)
Active blind use (avoid discomfort glare)
Active blind use (avoid discomfort
glare)
1500
1200
900
600
300
030 35 40 45 50 55 60
Daylight glare probability [%]
Num
ber
of w
orki
ng h
ours
ab
ove
DG
P
Imperceptible
Perceptible
Disturbing
Intolerable
DGPintolerable = 39%
1012 hours
Image by MIT OpenCourseWare.
Image by MIT OpenCourseWare.
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sz“ "
The formula makes sense. How plausible are DGP results compared to other glare indices?
Multidirectional Time-Lapse Simulation
Design: Jeff Niema
Paper J A Jakubiec, C F Reinhart, The Use of Glare Metrics in the Design of Daylit Spaces: Recommendations for Practice , submitted to Lighting Research and Technology 2011.
The image shows a cylindrical 360o view of a work space in Gund Hall. The color coded lines at the bottom show the predictions of different glare indices (DGP, DGI, UGI, CGI and VCP) whether discomfort glare will be experienced in a particular direction at different times of the day (Green=Imperceptible Glare; Yellow=Perceptible Glare; Orange=Disturbing Glare; Red=Intolerable Glare).
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Multidirectional Time-Lapse Simulation
Design: Jeff Niema
Paper J A Jakubiec, C F Reinhart, The Use of Glare Metrics in the Design of Daylit Spaces: Recommendations for Practice , submitted to Lighting Research and Technology 2011.
DGP yields most plausible results in these spaces.
How to analyze for visual discomfort?
Paper: J A Jakubiec, C F Reinhart, 2010, “The Use of Glare Metrics in the Design of Daylit Spaces: Recommendations for Practice", Lighting Research and Technology, 2011.
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sz“ "
sz“ "
‐ ‐
Multidirectional Time-Lapse Simulation
Design: Jeff Niema
Paper J A Jakubiec, C F Reinhart, The Use of Glare Metrics in the Design of Daylit Spaces: Recommendations for Practice , submitted to Lighting Research and Technology 2011.
DGP yields most plausible results in these spaces.
Concept of the Adaptive Zone
Design: Jeff Niema
Paper J A Jakubiec, C F Reinhart, The Use of Glare Metrics in the Design of Daylit Spaces: Recommendations for Practice , submitted to Lighting Research and Technology 2011.
Annual DG Calculation: Fixed view looking forwardAnnual DG Calculation: Fixed view looking forward
Annual DG Calculation: +/ 45 degrees rotational freedomAnnual DG Calculation: +/ 45 degrees rotational freedom
The concept helps to quantify the benefits of flexible furniture settings etc.
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Christoph ReinhartAssociate Professor email: [email protected]
The Ultimate Adaptive Space
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