Modeling Building Thermal Response to HVAC Zoning

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

6. Outline • Zoning Overview • Coordination Approach • Results

7. Outline • Zoning Overview • Coordination Approach • Results

8. “Snap-in” Zoning Retrofit

10. “Snap-in” Zoning Retrofit • Low cost • DIY: no configuration • Focus on forced air • Other systems are similar

21. Snap-in Zoning • Zoned Heat • K sensors • K heaters • K sensors • One heater • Central Heat • One sensor • 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. T D

29. When OFF: Train a dTk/dt =aT + ßD

30. When ON: Use a; Train ß dTk/dt =aT + ßD

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. T Time

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 Sookoor Kamin Whitehouse