1. Modeling Building Thermal Response to HVAC Zoning Virginia Smith Tamim Sookoor Kamin Whitehouse April 16, 2012 CONET Workshop (CPS Week)

2. Homes are ~30% vacant * National Academy of Science, 2006

3. Homes are ~30% vacant Smart Thermostat: 28% savings --Sensys 2010

4. Homes are ~50% used when occupied Ongoing work: Occupancy-driven Zoning

5. Ongoing work: Occupancy-driven Zoning Homes are ~50% used when occupied

6. Outline Zoning Overview Coordination Approach Results

7. Outline Zoning Overview Coordination Approach Results

8. “Snap-in” Zoning Retrofit

10. Low cost DIY: no configuration Focus on forced air Other systems are similar

21. Central Heat One sensor One heater Snap-in Zoning Zoned Heat K sensors K heaters K sensors One heater K+1 Control Signals Q: When the system turns on: Which damper configuration will achieve the desired temperature distribution?

22. Outline Zoning Overview Coordination Approach Results

26. Weather: Has a large effect on temperature Is not fully observable Rarely repeats Q: Can we learn the effect of dampers on temperature sensors without knowing the weather?

28. TD

29. dT k /dt =  a T + ß D When OFF: Train a

30. dT k /dt =  a T + ß D When ON: Use a ; Train ß

31. Outline Zoning Overview Coordination Approach Results

32. Experimental Approach Deployed zoning in a 7-room house 7 sets of dampers 12 thermostats Controlled based on occupancy 21 days of data

35. Time T

38. Conclusions “Snap-in” Zoning Cheap, easy, & energy saving Coordination btwn objects is needed Learning is complicated by weather ON/OFF separates weather/system

39. Credits & Questions Ginger Smith Tamim SookoorKamin Whitehouse