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SunCast : Fine-grained Prediction of Natural Sunlight Levels for Improved Daylight Harvesting

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

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SunCast : Fine-grained Prediction of Natural Sunlight Levels for Improved Daylight Harvesting

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  1. SunCast: Fine-grained Prediction of NaturalSunlight Levels for Improved Daylight Harvesting Jiakang Lu and Kamin Whitehouse Department of Computer Science, University of Virginia IPSN 2012

  2. Outline • Introduction • SunCast • Related work • Experiment • Evaluation • Limitation and future work • Conclusion

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. Step1: Similarity • Difference d between two days data • Similarity(weight)

  9. Step2: regression • Linear Regression • Y : current data, X:historical data, find a,b • Y* : predicted data, X:historical data

  10. Step3: creating distribution • Apply h historical traces • Produce prediction distribution x

  11. Window transparency • Wt : percentage of window transparency • 0% : closed, 100%:fully open • Objective function : • wSpeed: window switching speed • Maximum prediction window len

  12. 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

  13. Experiment • Two test bed : residential house and campus • House 4 weeks, campus 12 weeks

  14. Setup • Hobo data logger • Sensor node • Light • Temperature • Humidity • Sample/min

  15. 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

  16. Setpoint= 2000 lux • Energy : artificial lighting maintains the setpoint • Glare: harvested light above the target setpoint,

  17. Evaluation analysis • Impact of • Window switching speeds • window orientations • cloudiness levels

  18. Window switching speeds • Vary from 10~100 min

  19. window orientations

  20. cloudiness levels

  21. Improvement over reactive • SunCast has the largest effect on lighting stability • Experiment on four predictive feature window • Light stability improvement over reactive scheme

  22. Improvement over reactive

  23. Improvement over reactive

  24. limitation • Unpredictable • Sunrise • Sunset • Trees • Clouds • Nearby buildings • Environmental factors

  25. Future works • Merge data traces from multiple light sensors • Group estimation • Solar power system • Predict sunlight more opportunities for energy harvesting

  26. 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

  27. My Question • How many of historical data are enough? • Weather method v.s. predictive ?

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