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