1. SunCast: Fine-grained Prediction of Natural Sunlight Levels for Daylight Harvesting Jiakang Lu and Kamin Whitehouse Department of Computer Science University of Virginia April 18, 2012

3. 2 *US Energy Information Administration

4. 3 Daylight Harvesting

5. 4

6. 5

7. 6 Too responsive == annoying

8. 7 Not responsive == glare

9. 8 Too conservative == energy

10. 9 Daylight Harvesting

11. 10 Daylight Harvesting Disabled ( Too/not responsive )

12. 11 Daylight Harvesting Ineffective ( Too conservative ) Disabled ( Too/not responsive )

13. 12 Daylight Harvesting Reclaimed savings ( SunCast )

14. 13 Minimize: Energy + Glare s.t.: Switching Speed

15. 14 Find similar days in history Extract historical trends Create predictions Optimize window transparency

16. 15 Real-time Data Stream Historical Data Stream j - Distance - Similarity Find similar days in history

17. 16 Real-time Data Stream Historical Data Stream j Predicted Data Stream j - Regression - Prediction Map historical trends to current weather

18. 17 Create a prediction distribution

20. 19 Prediction Window Minimize: subject to:

21. 20 Two SunCast “knobs”: 1) If historical trace has error less than “β”: – ignore the historical trace 2) Limit daylighting % by “daylight weight” Remainder is electric lighting Daylight Electric

22. Evaluation 21

23. Evaluation Deployed HOBO light sensors 39 windows Up to 12 weeks Ran optimization on the data traces 22

24. Energy vs Glare 23 45% less glare 60% less energy waste SunCast-based daylight harvesting Vary β Vary Daylight Weight

25. Switching Times 24

26. Conclusions A novel sunlight prediction framework – continuous predictions over time – Distribution of predictions Enabling technology – Reclaim unrealized savings from daylight harvesting 25

27. Window Orientations 26

28. Mostly Cloudy Weathers 27 Clear OvercastPartly Cloudy