suncast: fine-grained prediction of natural sunlight levels for improved daylight harvesting jiakang...
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SunCast: Fine-grained Prediction of NaturalSunlight Levels for Improved Daylight
Harvesting
Jiakang Lu and Kamin WhitehouseDepartment of Computer Science, University
of VirginiaIPSN 2012
Outline
• 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
• 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 ?