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 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

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 ?