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Natural Light in DesignNatural Light in DesignUsing simulation tools to explore realistic daylight-responsive solutionsUsing simulation tools to explore realistic daylight-responsive solutions
Christoph Reinhart, Ph.D.Christoph Reinhart, Ph.D.
DaysimDaysim
Daylight Factor Visual Comfort
Daylight Autonomy Avoidance of Direct Sunlight
Required Work FlowSketchUp/ AutoCAD/ Ecotect/ …
Daysim Analysis www.daysim.com
Daysim Simulation Report
falsecolor maps
Example I – Single office
⟨ Located in Ottawa Canada
⟨ Taken from Daysim tutorial
Your Task: Use Daysim to:• Calculate daylight autonomy and daylight factor in the offices• Estimate the lighting energy savings from an occupancy sensor.
office building three lighting zones
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glob
al ir
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n [W
/m2 ]
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Development of a stochastic Model to calculate 1-min irradiances from 1 hour means
Solar Energy Walkenhorst, Reinhart 2002
Short-Time-Step Dynamics of Daylight
Step 1: Normalization
Short-Time-Step Dynamics of Daylight
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4 6 8 10 12 14 16 18 20 22time of day [h]
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2PM to 3PMShort-Time-Step Dynamics of Daylight
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glob
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Climate data – US DOEClimate data – US DOE
http://www.eere.energy.gov/buildings/energyplus/cfm/weather_data.cfm
Dynamic Daylight Simulations (DDS)As opposed to static DL simulations that only consider one sky condition at a time, dynamic daylight simulationsgenerate annual time series of interior illuminances and/orluminances.
How?
Daylight Coefficients
(1) Division of the Celestial Hemisphere
Eα(x)illuminance atx due to Sα
Sα
x
(2)Calculate Daylight Coefficients
climate Data: i
Energy & Buildings
Reinhart & Herkel 2000
Daylight coefficients: “fastest method & most accurate
dynamic method”
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Illum
inan
ce [l
ux]
reference case (12 days)
ADELINE (25 h)
classified weather data (20 h)
ESP-r (3 h)
DaySim (1.5 h)
Comparison of Dynamic Daylight Simulation Methods
Validation II
Daylight Coefficients: “same accuracy as standard Radiance”
Light. Res. & Technology Mardaljevic, 1997
Figure by MIT OCW.
0.0
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P_cell 1N_Skies 725
MBE% -3.2RMSE% 19.8
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m. F
req.
P_cell 2N_Skies 736
MBE% 1.0RMSE% 17.9
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P_cell 3N_Skies 741
MBE% 12.4RMSE% 19.9
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P_cell 4N_Skies 744
MBE% 1.7RMSE% 14.2
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Relative Error (%) Relative Error (%)50 100
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P_cell 5N_Skies 748
MBE% 5.7RMSE% 12.3
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P_cell 6N_Skies 751
MBE% -1.7RMSE% 11.3
C L E A R G L A Z I N G
Validation III - Venetian Blinds
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Day
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Aut
onom
y [%
] measuredsimulated
RADIANCE-based simulation approach
Energy & Buildings Reinhart, Walkenhorst 2001
Validation IV - Translucent Panel
Energy & Buildings Reinhart, Andersen 2005 (in review)
Need for a quality controlled material database.
Summary
Radiance combined with daylight coefficients and Perez sky model can efficiently and reliable calculate DDS. (validated approach – several independent studies –resulting accuracy ~20% rel. error – comparable to staticsimulations)
Radiance Simulation Parameters I
ambient bounces
ambient division
ambient sampling
ambient accuracy
ambient resolution
direct threshold
direct sampling
5 1000 20 0.1 300 0 0
simulation resolution =max scene dimensions x ambient accuracy
ambient resolution
Example:100m x 0.1
300 ~ 3cm (window mullion)
Radiance Simulation Parameters II
Higher raytraing parameters for blinds raytracing detail
ambient bounces
ambient division
ambient sampling
ambient accuracy
ambient resolution
direct threshold
direct sampling
7 1500 100 0.1 300 0 0
10m x 0.1 /300~0.3cm(blind slat)resolutionambient
accuracyambientxensionsceneimum dimmaxaa and ar:
Manual blind control model
work plane sensors
window with blinds) )
) ) )
) ) )
)
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⟨ 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 andwithout movable and/or fixed shading devices
Example I – Single officeDaylight factor simulation
⟨ same results for North and South offices⟨ no daylight on the central aisle
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
Occupacy Sensors
• IR: detect changing heat fields (work best when its cool inside)
• UV: detects changing movements (cannot differentiate between a fax machine and a person)
• wall, celing mounted, integrated in luminaire
• stand alone, connected to BAS
wall mounted
ceiling mounted
Lighting Controls I
Photocell-controlled Dimming with
Occupancy Sensor
Occupancy sensor.
Photocell.
Lighting Controls II
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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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
Example II - Museum LightingCIE 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
Example II: Seattle Art Museum -Arup Lighting using Daysim3D model of site and building
ARUP Lighting
Source: Matt Franks ‘Daylighting in Museum'shttp://irc.nrc-cnrc.gc.ca/ie/light/RadianceWorkshop2005/PDF/Franks_ArupCaseStudies.pdf
Example II - Seattle Art Museum -Arup Lighting using DaysimMuseum Open Hours - 1,500,000+ lux-hours ARUP Lighting
Example II - Seattle Art Museum -Arup Lighting using DaysimAutomatic Shading + Switching - 555,000 lh ARUP Lighting