6332 Day-Lighting Buildings
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Transcript of 6332 Day-Lighting Buildings
6332 Day-Lighting Buildings
Christoph ReinhartEnergy
Occupant BehaviorRules of Thumb
Energy Considerations
GSD 6332 – Occupant Behavior
Occupant Behavior Energy Considerations
Daylight Factor x Design Sky Daylight Autonomy
Course OutlineWeek 1: Course Introduction
Shading StudiesWeek 2: Climate Files
Daylighting Rules of ThumbWeek 3: Simulation I (Ecotect) Simulation II (Radiance)Week 4: Simulation III (more Radiance) Visit MIT – LAM & Partners Week 5: Simulation IV (Daysim) Occupant Behavior
Week 6: El. Lighting & Fixture Design Scale ModelsWeek 7: Midterm Project Critique Lighting Simulation IVWeek 8: Instructor away (no class) Field Trip - Visit KalwallWeek 9: Lighting Controls Toplighting (VELUX)Week 10: Case Studies (ARUP) Commissioning (LBNL)Week 11: Complex Fenestration Systems Thanksgiving (no class)Week 12: Aesthetics of light (Sampson) Art in SimulationWeek 13: Project Progress Review Light and HealthTBD: Final Project Critique
Oct 27 - Midterm Project CritiqueMisc
You will learn about …
occupant use of personal controls
Objective of today’s lecture
Review - Dynamic Daylight Simulations As opposed to a static simulation that only considers
one sky condition at a time, dynamic daylight simulations generate annual time series of interior illuminances and/or luminances.
Demo: Ecotect Export to Daysim- run static simulation- simulation parameters
- *ill files
Daylight Factor x Design Sky versus Daylight Autonomy
Daylight Factor x Design Sky Daylight Autonomy
Museum Lighting
Annual Light Exposure: established upper threshold for artwork – already established used used for museums (CIE TC3-22 ‘Museum lighting and protection against radiation damage’)
category material classification
example of materials
lighting illuminance
limiting annual exposure
I insensitive metal, stone, glass, ceramic
no limit no limit
II low sensitivity canvases, frescos, wood, leather
200 lux 600 000 lux h /yr
III medium sensitivity
watercolor, pastel, various paper
50 lux 150 000 lux h/yr
IV high sensitivity
silk, newspaper, sensitive pigments
50 lux 15 000 lux h/yr
Museum Lighting RequirementsCIE TC3-22 ‘Museum lighting and protection against radiation damage’
Example: Seattle Art Museum - Arup Lighting using Daysim 3D model of site and building
ARUP Lighting
source: http://irc.nrc-cnrc.gc.ca/ie/light/RadianceWorkshop2005/PDF/Franks_ArupCaseStudies.pdf
Example: Seattle Art Museum - Arup Lighting using DaysimSidelit Gallery ARUP Lighting
Example: Seattle Art Museum - Arup Lighting using Daysim
Museum Open Hours - 1,500,000+ lux-hours ARUP Lighting
Example: Seattle Art Museum - Arup Lighting using DaysimAutomatic Shading + Switching - 555,000 lh ARUP Lighting
Wrigley Global Innovation Center Chicago, Illinois – AEC
simulation: AEC
simulation: AEC
• Winter Garden Atrium break area
• Views from adjacent offices
• Illuminance hours requirements for ficus trees
simulation: AEC
Wrigley Global Innovation Center Chicago, Illinois - AEC
simulation: AEC
Occupant Behavior
0
20
40
60
80
100
0.5 1 1.5 2 2.5 3.3 4.5 5.5 6.5 7.5distance to facade [m]
dayl
ight
aut
onom
y [%
]
blinds always fully closed
blinds always down, slats at 45 o
blinds always up
USER BEHAVIOUR ?!
architecture: Meier-Weinbrenner-Single, Nürtingen
• passive house standard• advanced glazing• SHW, PV
• ventilation heat recovery• ground heat exchanger • night ventilation
Reinhart, Voss 2003Monitoring User Behavior
HOBO data logger
IlluminanceTemperature
occupancy
Monitoring Setup in the Offices
receiver2414.5 MHz
data acquisitionEIB systemBlind setting
video surveillance camera
Monitoring Blind Usage
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
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
type 1
type 2
Jim Love, University of Calgary
Switch-On Probability (I)
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
Hunt 1978Lamparter 2000
Switch-On Probability (II)
0
0.2
0.4
0.6
0.8
1
0 100 200 300 400 500minimum work plane illuminance [lux]
switc
h-on
pro
babi
lity
at a
rriv
al
People are Consistent but Different
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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 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 controls
0
0.25
0.5
0.75
1
switc
h-of
f pro
babi
lity
whe
n le
avin
g no controlsdimmed systemoccupancy sensor
<30 minutes30-59 minutes
1-2 hours2-4 hours
4-12 hours12-24 hours
24+ hours
Switch-Off Probability
Manual blind control model
Daysim: active (energy conscious) or passive user
Associate work plan sensor with window
Note: this step requires to couple individual sensors together.
Benefit: Direct comparison between daylighting concepts with and without movable and/or fixed shading devices
work plane sensors
window with blinds
) )
) ) )
) ) )
)
) ) )
annual occupancyprofiles
annual illuminanceprofiles
Lightswitch 2002
Lightswitch Algorithm(stochastic)
el. lighting/blinds profile
Reinhart, 2002
Model Overview
Switch lights on
Is work place already occupied?
YES
stochastic process:switch on probability
(Fig. 7-5)
NO
NO
YES
NOYES
Has the room been deserted for longer than sensor
delay time?
Are lights switched on?
Does occupant arrive?
Is there an occupancy sensor?
YES
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
Does occupant leave?
NO
YES
stochastic process:intermediate switch on
probability (Fig. 7-7)
NO YES
Switch lights on
Does occupant arrive?YES
NO
NO0
0.2
0.4
0.6
0.8
1
0 100 200 300 400 500 600minimum work plane illumiance [lux]
switc
h-on
pro
babi
lity
HuntLamparter
stochastic process:switch on probability
Manual Lighting Control Algorithm
field study approach
Example I – single office
Located in Ottawa Canada
Taken from Daysim tutorial
office building three lighting zones
Example I – single office
same results for North and South offices no daylight on the central aisle
Daylight factor simulation
Example I – single office
ample amount of daylight in both offices up to 30% DA on aisle => on/off switch with timer
Daylight Autonomy simulation
Lighting ControlsPhotocell-controlled Dimming with Occupancy Sensor
Occupancy sensor.
Photocell.
Demo: Daysim- active and passive behavior
Example I – single office
absolute comparison of different control strategies reference case is manual on/off switch with venetian blinds
Electric Lighting Use in South facing Office
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0.5
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1.5
2
2.5
3
manual on/off switch switch-off occupancysensor
on/off occupancysensor
dimming system dimming system withswitch-off occupancy
sensor
dimming system withon/off occupancy
sensor
annu
al e
lect
ric li
ghtin
g us
e [k
Wh/
ft2 yr]
reference case
By Oct 23rd: Work through the rest of the Getting Started document Read through Daylighting Metrics Paper Read Daysim tutorial pages 18 -32 Voluntarily: Read Rendering with Radiance Chapters 10 - 13
Reading:
Questions…