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

1

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.


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

3

Image by MIT OpenCourseWare.


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

4


“”

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


1-2 hours2-4 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


1-2 hours2-4 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


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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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http://upload.wikimedia.org/wikipedia/commons/f/f5/Eyesection.svg


"

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.


"

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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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


""

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



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.


“ ”

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




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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sz“ "


Design: Jeff Niema


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“ "

‐ ‐


Design: Jeff Niema


DGP yields most plausible results in these spaces.

Concept of the Adaptive Zone

Design: Jeff Niema


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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lecture 13: visual comfort & occupant behavior

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