Modeling Building Thermal Response to
HVAC ZoningVirginia SmithTamim Sookoor
Kamin Whitehouse
April 16, 2012CONET Workshop (CPS Week)
Homes are ~30% vacant
* National Academy of Science, 2006
Homes are ~30% vacant
Smart Thermostat: 28% savings--Sensys 2010
Homes are ~50% usedwhen occupied
Ongoing work:Occupancy-driven
Zoning
Ongoing work:Occupancy-driven
Zoning
Homes are ~50% usedwhen occupied
Outline
•Zoning Overview
•Coordination Approach
•Results
Outline
•Zoning Overview
•Coordination Approach
•Results
“Snap-in” Zoning Retrofit
“Snap-in” Zoning Retrofit
•Low cost
•DIY: no configuration
•Focus on forced air
•Other systems are similar
•Central Heat
•One sensor
•One heater
Snap-in ZoningZoned Heat
•K sensors
•K heaters
•K sensors
•One heater
•K+1 Control Signals
Q: When the system turns on:
Which damper configuration will achievethe desired temperature distribution?
Outline
•Zoning Overview
•Coordination Approach
•Results
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?
T D
dTk/dt = aT + ßD
When OFF:Train a
dTk/dt = aT + ßD
When ON:Use a; Train ß
Outline
•Zoning Overview
•Coordination Approach
•Results
Experimental Approach
•Deployed zoning in a 7-room house
•7 sets of dampers
•12 thermostats
•Controlled based on occupancy
•21 days of data
Time
T
Conclusions•“Snap-in” Zoning
•Cheap, easy, & energy saving
•Coordination btwn objects is needed
•Learning is complicated by weather
•ON/OFF separates weather/system
Credits & Questions
Ginger Smith Tamim Sookoor Kamin Whitehouse
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