The Self-Programming Thermostat: using occupancy to optimize setback schedules Kamin Whitehouse Joint work with: Gao Ge BuildSys University of Virginia Nov
The Self-Programming Thermostat: using occupancy to optimize setback schedules Kamin Whitehouse Joint work with: Gao GeBuildSys University of Virginia.
Dec 14, 2015
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The Self-Programming Thermostat: using occupancy to
optimize setback schedulesKamin Whitehouse
Joint work with: Gao Ge BuildSysUniversity of Virginia Nov 3, 2009
Problem Definition
Goal in US: Reduce energy usage in existing buildings
by 25% by 2020
Problem Definition
Problem Definition
• But reducing HVAC energy --> $$– Insulation, new windows, solar panels,
geothermal, HVAC upgrades, etc.– All require $1000’s and take many years for
ROI
• Federal stimulus: $5 billion for weatherization of low-income homes– Small % of target savings
• We need low-cost energy solutions
State of the ArtSetback Schedules
• Widely-accepted• Cost-effective• But still largely untapped potential!
– Why?
Time
Tem
p
Setpoint
Setback
State of the Art
Too much hassle!X
Too much hassle!X
Occupancy
State of the Art
Occupancy
Time
Tem
p
Setpoint
Setback
Start time End time
Miss time
Self-Programming Thermostat
Occupancy Occupancy
Tem
p
Occupancy Occupancy
Occupancy Occupancy
Occupancy Occupancy
Occupancy Occupancy
Occupancy Occupancy
Leave Distribution Arrive Distribution
Self-Programming Thermostat
Time
Tem
p
Leave Distribution Arrive Distribution
Self-Programming Thermostat
Time
Tem
p
Leave Distribution Arrive Distribution
1. User specifies miss time2. Thermostat maximizes setback period wrt miss time
Self-Programming Thermostat
Time
Tem
p
Leave Distribution Arrive Distribution
0 Hrs miss time
Self-Programming Thermostat
Time
Tem
p
Leave Distribution Arrive Distribution
0.5 Hrs miss time
Self-programming Thermostat
Time
Tem
p
Leave Distribution Arrive Distribution
1 Hr miss time
Self-Programming ThermostatMiss time knob allows user to navigate the Pareto optimal set of schedules
User Interface
• Three knobs: setpoint, setback, miss time
• As the user tunes the knobs, the system displays resultant schedule and energy usage
• Result: explicit energy/comfort trade-off– controllable and predictable – not smart!
Sensing Occupancy
Time of day from 0 hours (12 AM)
Front Door
Bathroom Motion
Kitchen Motion
Bedroom Motion
Sense occupancy • $50-100 per home• No cameras/tags
Sensing Occupancy
True Positive Rate
Event Detection Rate
Duration Accuracy
UbiComp ‘08
Evaluation
• Two publicly-available data sets– Kasteren– Tulum• (not a random sample)
– Both ~1 month – Hand-labeled many activities– We used “leave home” and “return home”
Evaluation
Summary• Use sensors to identify occupancy• Automatically tune setback schedules• Use miss time knob to navigate Pareto set
• Benefits– Simple interface– More energy savings; same comfort– More comfort, same energy savings– Cheap! $50-$100 per home
Other Related Work
• Reactive Thermostats– Similar to motion-sensor triggered lights
• Microenvironments– User-controlled local conditioning
• Facilities management and building operators
Future Work
• More users, deployments and Energy– Spoiler alert! Results still good with 44 users
and 8 homes with sensors, w/ heat pump
• Micro-zoning control• Other building types• Market penetration: UI & Economics• .
Questions?
Deployment Details for FATS Demonstration
• Eight homes deployed with wireless X10 sensors for at least 7 days with an X10 receiver to record messages
• Four diverse single person homes, four diverse multi-person homes
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