Thesis Midterm Presentation

Occupant Behavior

MDesS Thesis Midterm | Cynthia Kwan | Advisor Christoph Reinhart |

Current Dominant Daylight Metrics
Daylight Factor method prevalent in the building industry
 existed for a long time, easy to use, also used in LEED certification

Shortcoming of the Daylight Factor method:
 Only considers worst case scenario (illuminance of overcast sky)
 Task surface illuminance : sky illuminance
 Assumes the identical sky conditions of same latitude locations
(ignores sunny or cloudy climate)

Daylight Factor Calculation

DF = 100 * Ein / Eext

Ein = Inside illuminance at a fixed point
Eext = Outside horizontal illuminance under an
overcast (CIE sky) or uniform sky.

http://www.learn.londonmet.ac.uk/packages/clear/visual/daylight/analysis/hand/daylight_factor.html

MDesS Thesis Midterm | Spring 2009 | Cynthia Kwan |

Climate-based Metrics

Arguments for climate-based metrics:

 Previously mentioned limitations of DF as a design metric
 Climate-based metrics allow to custom-tailor the analysis with
regards to local climate and occupant use of a space


Climate-based Metrics – Daylight Autonomy
 Uses Radiance as the calculation engine
 Physically correct backward ray-tracing method http://www.contrib.andrew.cmu.edu/~yihuang/ls/raytrace.jpg

 Light source = imaginary sky dome divided into 145 segments
 Illuminance of each patch defined by direct & diffuse irradiances in
the weather data file (Perez Sky Model,1993)
 Sky conditions can be converted down to 1-minute time step
 min. time-resolution of task surface illuminance simulation

Image source: Reinhart, Christoph F. (2006). Tutorial on the Use of DaySim Simulation for Sustainable Design. Ottawa, Canada: National Research Council - Institute for Research in Construction. P.25.


Climate-based Metrics - Daylight Autonomy

Barrier towards the use of climate-based metrics:

 Access to simulation software ($$ & initial training)
 Time
 A lack of published data as to what constitutes a high, medium or
low daylight autonomy level in various space types.


Research Question

What constitutes a „good‟ Daylight Autonomy distribution in
a Gymnasium?


Methodology
 Compare simulation results of a series of real spaces
 Ecotect models of real world projects created by translucent panel
manufacturer Kalwall
 170+ models including atria, classrooms, offices, gymnasia,
museums and libraries.
 Select one category of spaces (gymnasia) for detailed study
• Reasonable number of cases (30) available
• Nature of activity requires certain controlled lighting
characteristics which can be provided by daylight
• Use of active shading devices uncommon
 eliminate one uncertainty factor in the simulations and study
 Base evaluation on existing IESNA standard


Methodology
1. Quality and consistency check for the selected 30 Ecotect
gymnasiums models (take model as is assuming no obstruction)
2. Set up photo sensor grid in Ecotect
3. Export model to DaySim and load in corresponding weather EPW
4. Run annual illuminance simulation
5. Run Daylight Autonomy analysis with target illuminance advised in
the IESNA RP-6-01 Sports & Recreational Area Lighting standard
6. Calculate Uniformity Ratio (UR; max/min illuminances) by Excel
spreadsheet functions
7. Compile graphs for DF, DA and UR for all spaces and derive the
average numbers
8. Each model (space) will be scored according to its performance
rank in each category (or a credits matrix)
9. Account for and discuss high-scoring and low-scoring designs.


IESNA RP-6-01 Sports & Recreational Area
Lighting
Criteria for basketball facility:

 Target Illuminance
500 lux for Class III (Some provisions for spectators)
300 lux for Class IV (No provision for spectators)

 Uniformity ratio (max/min illuminance)
≤3.0 for Class III (Some provisions for spectators)
≤4.0 for Class IV (No provision for spectators)

 Glare avoidance


30 Gyms in the US and Europe

Alameda, CA Muncie, IN
Gloucester, UK
Bedford, MA Omaha, NE
Gloversville, NY
Berea, OH Palmdale, CA
Greensburg, PA
Bronx, NY Ravenna, OH
Holland Patent, NY
Brownsville, VA Scottsdale, PA
Lake Los Angeles, CA
Clouston, WV Spokane, WA
Largo, FL
Colbert, WA Syracuse, NY
Lewiston, NY
Elgin, Scotland, UK Syracuse, NY
Maldegem, Belgium
Fort Wayne, IN Utica, NY
Mars, PA
Foxburg, PA Walton, NY
Maywood, CA



(Made by Google My Maps)


 Gymnasia of similar sizes from across the US and Europe

Greensburg, PA Colbert, WA Brownsville, VA Bedford, MA
Area = 784 m2 Area = 534 m2 Area = 699 m2 Area = 772 m2
Class IV Class IV Class IV Class III

Largo, FL Alameda, CA Gloucester, United Kingdom Omaha, NE
Class IV Class III Class IV Class III

Bronx, NY Berea, OH Maldegem, Belgium
Area = 1151 m2 Area = 502 m2 Area = 2440 m2
Class III Class IV Class III

 Gymnasia of similar sizes from across the US and Europe

Clouston, WV Elgin, Scotland Fort Wayne, IN Foxburg, PA
Area =1397 m2 Area = 1440 m2 Area = 6537 m2 Area = 731 m2
Class III Class III Class III Class III

Mars, PA Scottsdale, PA Muncie, IN Utica, NY
Class IV Class III Class III Class III

Gloversville, UK Holland Patent, NY Walton, NY
Area = 1577 m2 Area = 698 m2 Area = 850 m2
Class III Class III Class III

Model Consistency Check
 Roof depth = 450 mm
 Wall thickness = 150 mm
 Material definitions (especially translucent and transparent)
• KalwallTranslucent06,08,12,15,20 validated; 30 approximated
• Clear40, GenericDoubleGlazing72
• CeilingForLighting = 80% reflectance
• WallForLighting = 50% reflectance
• FloorForLighting = 30% reflectance
 Material naming consistency, export library
 Set up visualization camera for Radiance rendering
(Ambient bounces 4; Ambient division 1000)


Example Simulation
 Sensor point grid set-up:
Work plane defined at 1.6m (typical eye level)
Grid extend = 15.24 m x 28.65m (full basketball court)
Node points = 7 x 13 = 91 points (~2x2 m grid)


Simulation Parameters
 Annual illuminance simulation parameters (for DaySim):

Ambient bounces 7
Ambient division 1500
Ambient sampling 100
Ambient accuracy 0.1
Ambient resolution 300
Direct Threshold 0
Direct sampling 0

(Other parameters kept at default)


DA Analysis Parameters

 Daylight Autonomy (DA) analyses parameters:

Time frame: 07.00 - 18.00
(time step = 60 minutes)
No lunch breaks
Minimum illuminance level:
500 lux (Class III)
300 lux (Class IV)


Uniformity Ratio
 Import ILL file into Excel spreadsheet
 Find minimum and maximum lux for each time step (each row)
 Find ratio of max/min
 Find % of time the required ratio is met (when daylight possible)


Results – DA and Mean DA


Results – DA vs Daylight Factor
>2%


Results – DA vs Uniformity Ratio

% of time meeting
Uniformity Ratio


High-scoring Designs

DF ≥ 2% = 12%
Mean DA = 92.5%
Mars, PA Uniformity met = 99.6%

DF ≥ 2% = 100%
Mean DA = 89.5%
Uniformity met = 96.8%
Berea, OH


Low-scoring Designs

DF ≥ 2% = 0%
Mean DA = 22.65%
Omaha, NE Uniformity = 75.84 %

DF ≥ 2% = 0%
Mean DA = 33.5%
Scottsdale, PA
Uniformity met = 50.7%


Data Analysis

% of time meeting
Uniformity Ratio


Data Analysis


Data Analysis – Next Steps
Each case could be fully accounted for
Discuss extreme cases (e.g. seemingly mismatched DA, DF and UR)

Point Scoring System using multiple (weighed) criteria:
 DA, DF and UR calculated
 Continuous DA, i.e. a sensor illuminance reading will still receive
partial credits even if it falls short of the target lux level (300 or 500)
e.g. if a sensor only receives 240 lux when the target is 300 lux, it
still receive partial credit = 240/300 = 0.8
 Hunt probability – likelihood that a user will flip on the light switch
based on the initial assessment of the space at point of entry
(based on office occupancy behavior  transferable to gym?)


Discussion Questions
 Applying metrics geared towards electrical lighting to daylighting
 Significance of weather data
 (Use of climate analyzer)
 Metrics displayed a wide range of results
- Average DA range 39.5 – 70.9%
- Overall Average DA = 55.2%
- Average 67.9% of time meeting uniformity ratio
 Use of Hunt probability (office user pattern applied to gyms)
 Use of continuous DA (rationale described in methodology)
 Rules of weighing score criteria


Discussion Questions
Preliminary design guidelines:

 High-scoring designs for Daylight Autonomy + Uniformity
- Continuous ribbon windows at the crown (clerestory)
- Skylights in the center

 Adding side windows enhances overall light level, but hurts
uniformity

 Climate-responsiveness
 Environment – obstructions not included in this study


Deliverables

 A paper describing the methodology, results and discussions of this
study, with detailed case studies for each of the 30 gymnasium in the
appendix

 A standard for what is a “good” Daylight Autonomy and Uniformity
Ratio for Gymnasia

 [Design guidelines on how to achieve good daylighting in gymnasia
to meet DA levels advised by the study]


References
Bourgeois, D. , C.F. Reinhart & G. Ward (2008) A Standard Daylight Coefficient Model for Dynamic Daylighting
Simulations. Building Research and Information, 36(1), 68-82.
Galasiu, A.D. & C.F. Reinhart (2008). Current Daylighting Design Practice: A Survey. Research & Information,
36(2), 159-147. doi: 10.1080/09613210701549748.
Guzowski, Mary (2000). Daylighting for sustainable design. New York : McGraw-Hill.
IESNA Sports and Recreational Area Lighting Committee (2001). IESNA RP 6-01:Recommended Practice for
Sports and Recreational Area Lighting. New York: IESNA.
Lee , E.S. & S.E. Selkowitz (2006). The New York Times Headquarters daylighting mockup: Monitored
performance of the daylighting control system. Energy and Buildings, 38, 914–929.
Perez R., Seals R. and Michalsky J. (1993) All-weather model for sky luminance distribution – preliminary
configuration and validation. Solar Energy, 50, 235-245.
Reinhart, Christoph F. & Valerio M. LoVerso (2007). Rules of Thumb Based Design Sequence for Diffuse
Daylighting, Lighting Research and Technology.
Reinhart, Christoph F. & Marilyne Andersen (2006). Development and validation of a Radiance model for a
translucent panel. Energy and Buildings, 38, 890–904.
Reinhart, Christoph F., John Mardljevic & Zsck Rogers (2006). Dynamic Daylight Performance Metrics for
Sustainable Building Design. Leukos, 3(1), 7-31.
Reinhart C. F. & S. Herkel (2000) The simulation of annual daylight illuminance distributions – a state-of-the-art
comparison of six RADIANCE-based methods. Energy and Buildings, 32, 167-187.
Reinhart, Christoph F. (2006). Tutorial on the Use of DaySim Simulation for Sustainable Design. Ottawa, Canada:
National Research Council - Institute for Research in Construction.
Walkenhorst, O., J. Luther, C. Reinhart & J. A.Timmer (2002). Dynamic annual daylight simulations based on
one-hour and one-minute means of irradiance data. Solar Energy, 72(5), 385-395.


Thank you!


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