Transcript SunCast: Fine-grained Prediction of Natural Sunlight

SunCast: Fine-grained Prediction of
Natural
Sunlight Levels for Improved
Daylight Harvesting
Jiakang Lu and Kamin Whitehouse
Department of Computer Science,
University of Virginia
IPSN 2012
Outline
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Introduction
SunCast
Related work
Experiment
Evaluation
Limitation and future work
Conclusion
Introduction
• Artificial lighting consumes 26% energy in
commercial building
• Daylight harvesting is the approach of using
natural sunlight
– Reduce lighting energy by up to 40%
– Smart glass
– Not stable
– Caused glare(刺眼) and discomfort
Daylight harvesting
• Nature sunlight changed rapidly
– 50% existed systems are disables by users
– Window transparency changed slowly
• Window change speed v.s. daylight change
speed
– Glare
– Energy waste
• Problem
– How to minimize both glare and energy usage
Objective
• SunCast
– Prediction natural sunlight level
• Fine grained
– Control the window transparency
• Adjust in advance
– Purely data-driven approach to create distribution
– Instead of making an explicit environment model
Related work
• Predict average sunlight over time period
• Weather forecast : only predict cloudiness in
the sky, can not predict the effect of shadow
at particular locations
• Control system need more fine-grained
information instead of forecast websites
SunCast
• Predicting sunlight values :3 steps
– calculates the similarity between the real-time
data stream and historical data traces
– uses a regression analysis to map the trends in the
historical traces to more closely match patterns of
the current day
– combines the weighted historical traces to predict
the distribution of sunlight in the near future
Step1: Similarity
• Difference d between two days data
• Similarity(weight)
Step2: regression
• Linear Regression
• Y : current data, X:historical data, find a,b
• Y* : predicted data, X:historical data
Step3: creating distribution
• Apply h historical traces
• Produce prediction distribution x
Window transparency
• Wt : percentage of window transparency
– 0% : closed, 100%:fully open
• Objective function :
• wSpeed: window switching speed
• Maximum prediction window len
Prediction and reaction
• Prediction algorithm is ideal for rapid sunlight
changes
• Stable sunlight, window transparency control
has better performance based on current
sunlight condition
• Hybrid scheme : switch smoothly between
prediction and reaction according β
• β is light error threshold
Experiment
• Two test bed : residential house and campus
• House 4 weeks, campus 12 weeks
Setup
• Hobo data logger
• Sensor node
– Light
– Temperature
– Humidity
– Sample/min
Other methods
• Reactive
– periodically measures the current daylight and sets
window transparency to come as close to the target
setpoint as possible
• Weather
– Select the same cloudiness level from historical data as
• Oracle
– Using the actual future light values instead of predicted
values
• Optimal
– Control window transparency directly
Setpoint= 2000 lux
• Energy : artificial lighting maintains the setpoint
• Glare: harvested light above the target setpoint,
Evaluation analysis
• Impact of
– Window switching speeds
– window orientations
– cloudiness levels
Window switching speeds
• Vary from 10~100 min
window orientations
cloudiness levels
Improvement over reactive
• SunCast has the largest effect on lighting
stability
• Experiment on four predictive feature window
• Light stability improvement over reactive
scheme
Improvement over reactive
Improvement over reactive
limitation
• Unpredictable
– Sunrise
– Sunset
– Trees
– Clouds
– Nearby buildings
– Environmental factors
Future works
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Merge data traces from multiple light sensors
Group estimation
Solar power system
Predict sunlight more opportunities for energy
harvesting
Conclusion
• SunCast
– Continuous prediction over time
– Distributions of prediction
• Predictive window control scheme
– Reducing glare 59%
– Saving more energy by artificial lighting
• Applied to other applications
– Highway traffic prediction
– City pollution levels
– Building occupancy
My Question
• How many of historical data are enough?
• Weather method v.s. predictive ?