The Self-Programming Thermostat: using occupancy to optimize setback schedules Kamin Whitehouse Joint work with: Gao GeBuildSys University of Virginia Nov 3, 2009

Problem Definition Goal in US: Reduce energy usage in existing buildings by 25% by 2020

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 Art Setback Schedules Widely-accepted Cost-effective But still largely untapped potential! –Why? Occupancy Time Temp Setpoint Setback

State of the Art Too much hassle! X X

Occupancy State of the Art Occupancy Time Temp Setpoint Setback Start timeEnd time Miss time

Self-Programming Thermostat Occupancy Time Temp Setpoint Setback Start timeEnd time Miss time Occupancy Leave DistributionArrive Distribution

Self-Programming Thermostat Occupancy Time Temp Setpoint Setback Start timeEnd time Miss time Occupancy Leave DistributionArrive Distribution

Self-Programming Thermostat Occupancy Time Temp Setpoint Setback Start timeEnd time Miss time Occupancy Leave DistributionArrive Distribution 1. User specifies miss time 2. Thermostat maximizes setback period wrt miss time

Self-Programming Thermostat Occupancy Time Temp Setpoint Setback Start timeEnd time Miss time Occupancy Leave DistributionArrive Distribution 0 Hrs miss time

Self-Programming Thermostat Occupancy Time Temp Setpoint Setback Start timeEnd time Miss time Occupancy Leave DistributionArrive Distribution 0.5 Hrs miss time

Self-programming Thermostat Occupancy Time Temp Setpoint Setback Start timeEnd time Miss time Occupancy Leave DistributionArrive Distribution 1 Hr miss time

Self-Programming Thermostat Miss 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 $ 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