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