Jiakang Lu, Tamim Sookoor, Vijay Srinivasan, Ge Gao, Brian Holben, John Stankovic, Eric Field, Kamin Whitehouse SenSys10

Abstract Motivation Challenges Introduction Design Experiment Setup Evaluation Conclusion

Abstract Motivation Challenges Introduction Design Experiment Setup Evaluation Conclusion

Heating, ventilation and cooling(HVAC) is the largest source of residential energy consumption Using cheap($5 each) and simple sensors(motion, door sensors) to automatically sense the occupancy and sleep patterns in a home Automatically turn on or off the HVAC system Achieve 28% energy saving on average, at a cost of approximately $25 in sensors Commercially-available baseline approach that use similar sensors saves only 6.8% energy on average

HVAC is the single largest contributor to a homes energy bills and carbon emission Accounting for 43% of residential energy consumption in the U.S. and 61% in Canada and U.K., which have colder climates Recent studies show that households with programmable thermostats have higher energy consumption on average than those with manual controls Users program them incorrectly or disable them altogether

Quickly and reliably determine when occupants leave the home or go to sleep Motion sensor are notoriously poor occupancy sensors, which often turn lights off when a room is still occupied When to turn HVAC system on Too early or too late, both waste energy

Abstract Motivation Challenges Introduction Design Experiment Setup Evaluation Conclusion

A 2-stage heat pump and a third stag electric heater Stage 2 is the most efficient stage, but with longer response time Stage 3 has the fastest response time but high energy cost Stage 1 operates at a lower power level, it is more effective at maintaining a constant temperature

Problems Occupants leave home shortly after 9AM, but the system continues heating until 10AM Shallow setback( typically 5 degrees Celsius) Comfort loss The risk of comfort loss causes people to reduce their use of setback schedules

Uses motion sensors, door sensors, or card key access systems to turn the HVAC on and off 4 out of 8 households actually increase energy usage by up to 10% Leave at 9:30AM turn off at 10:30AM Shallow setback Inefficient stage of heating

Fast reaction algorithm uses a probabilistic model to process the sensor data to estimate the occupancy Preheating Deep setback( about 10 degrees Celsius)

Abstract Motivation Challenges Introduction Design Experiment Setup Evaluation Conclusion

Passive infrared(PIR) motion sensors in rooms Magnetic reed switches on entry ways Approximately $5 each

Target preheat time t Arrival time a Tulum dataset[13], which was created by monitoring the occupants of a home for approximately one month a < t heat with stage 3 a > t heat with stage 2 optimum preheating time 18:06

T last the time elapsed since the last sensor firing It changes to idle state when its idle time is over a threshold It changes to active state when it detect an event Sleep from 10PM to 10AM

Estimate the probability of 3 states of the home Away, Active, Sleep HMM transitions to a new state every 5 minutes x t is a vector of 3 features of sensor data time of day at 4-hour granularity total number of sensor firings in time interval dT binary features to indicate presence of front door, bedroom, bathroom, kitchen, and living room sensor firings in dT Training the model

Abstract Motivation Challenges Introduction Design Experiment Setup Evaluation Conclusion

Empirical data traces from 8 instrumented homes Occupant surveys of 41 homes(4 weeks) Two public smart home dataset Tulum, Kasteren One motion sensor in each room One door sensor on each entryway to the home, and some inner doors

Daily interviews with the residents to clarify ambiguous or questionable data Not perfectly accurate Previous studies have used approaches ranging from self reports to video camera recordings None of these schemes for creating ground truth are expected to be perfect

Empirical data traces from 8 instrumented homes Occupant surveys of 41 homes(4 weeks) Two public smart home dataset Tulum, Kasteren Each individual wrote down their sleep, wake, leave, and arrive times every day Retirees, students, professionals, young professionals, and families

Empirical data traces from 8 instrumented homes Occupant surveys of 41 homes(4 weeks) Two public smart home dataset Tulum, Kasteren Use only the leave, arrival, and sleep event labels

Performance can be affected by outdoor temperature, air leakage, and house insulation Evaluate different thermostat algorithms under different housing conditions and climates Weather data are from the local airport weather station that provides hourly data

Abstract Motivation Challenges Introduction Design Experiment Setup Evaluation Conclusion

Threshold, slow reaction, treat inactive as active Threshold, fast reaction, treat active as inactive HMM 88% accuracy React5 78% accuracy

Run 14 days in January and July using the climate in Charlottesville, VA to evaluate both cooling and heating. Deep setbacks to 10°C for heating and 40°for cooling

Reactive thermostat wastes energy due to frequent reactions Reactive saves 2.9kWh(6.8%), misses 60 mins on average Smart saves 11.8kWh(27.9%), misses 48 mins on average

Using data from home B Fast reaction, Deep setback, Preheating Add deep setback slightly increase miss time Save 34% energy, improve miss time by 51 mins

Almost negligible Selected vs. All energy 28.9% vs. 23.6% miss time 54 mins v.s 48 mins Selected set performs better!!!???

Periodic vs. Aperiodic life style Save more energy and miss less time for periodic life style

As climate becomes warmer from MN to TX, it approaches the optimal scheme Deep setbacks are beneficial during the day warm region, peak loads at mid-day cold region, night Cold region consumes much more energy Lowering same amount of set point 5-8 degrees will only reduce the energy by a fraction

Smart thermostat that senses occupancy statistics in a home to save energy through improved control of the HVAC system Very low cost less than $25 per home save 28% HVAC energy Although it just saves $15 per month for a family, it has nationwide savings about $15 billion annually, and prevent 1.12 billion tons of pollutants from being released into the air.

Q&A Thanks~