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

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

SenSys’10

The Smart Thermostat: Using Occupancy Sensors to Save

Energy in Homes

AbstractMotivationChallengesIntroductionDesignExperiment SetupEvaluationConclusion

Outline


Outline

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 systemAchieve 28% energy saving on average, at a

cost of approximately $25 in sensorsCommercially-available baseline approach

that use similar sensors saves only 6.8% energy on average

Abstract

HVAC is the single largest contributor to a home’s energy bills and carbon emissionAccounting 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 controlsUsers program them incorrectly or disable

them altogether

Motivation

Quickly and reliably determine when occupants leave the home or go to sleepMotion sensor are notoriously poor occupancy

sensors, which often turn lights off when a room is still occupied

When to turn HVAC system onToo early or too late, both waste energy

Challenges


Outline

A 2-stage heat pump anda third stag electric heater

Stage 2 is the most efficientstage, but with longer response time

Stage 3 has the fastestresponse time but high energy cost

Stage 1 operates at a lower power level, it is more effective at maintaining a constant temperature

HVAC stage

ProblemsOccupants leave home shortly after 9AM, but

the system continues heating until 10AMShallow setback ( typically 5 degrees Celsius)Comfort loss

The risk of comfort loss causes people to reduce their use of setback schedules

Programmable Thermostat

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 10:30AMShallow setbackInefficient stage of heating

Reactive Thermostat

Fast reaction algorithm uses a probabilistic model to process the sensor data to estimate the occupancy

PreheatingDeep setback( about 10 degrees Celsius)

Smart Thermostat


Outline

Passive infrared(PIR) motion sensors in rooms

Magnetic reed switches on entry waysApproximately $5 each

Hardware

Target preheat time tArrival time aTulum dataset[13], which was created by

monitoring the occupants of a home for approximately one month

a < t heat with stage 3a > t heat with stage 2optimum preheating time 18:06

Preheating

Tlast the time elapsed since the last sensor firing

It changes to idle state when it’s idle time is over a threshold

It changes to active state when it detect an event

“Sleep” from 10PM to 10AM

Reactive State Machine

Estimate the probability of 3 states of the homeAway, Active, Sleep

HMM transitions to a new state every5 minutes

xt is a vector of 3 features of sensor datatime of day at 4-hour granularitytotal number of sensor firings in time interval dTbinary features to indicate presence of front

door, bedroom, bathroom, kitchen, and living room sensor firings in dT

Training the model

Hidden Markov Model(HMM)


Outline

Empirical data traces from 8 instrumented homes

Occupant surveys of 41 homes(4 weeks)Two public smart home dataset

Tulum, KasterenOne motion sensor in each roomOne door sensor on each entryway to the

home, and some inner doors

Collecting Occupancy data

Daily interviews with the residents to clarify ambiguous or questionable data

Not perfectly accuratePrevious 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

Ground Truth



Tulum, KasterenEach individual wrote down their sleep, wake,

leave, and arrive times every dayRetirees, students, professionals, young

professionals, and families




Tulum, KasterenUse 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

EnergyPlus Simulator


Outline

Threshold ↑, slow reaction, treat inactive as active

Threshold ↓, fast reaction, treat active as inactive

HMM 88% accuracyReact5 78% accuracy

HMM vs. Reactive Algorithm

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

Evaluation

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

Evaluation

Using data from “home B”Fast reaction, Deep setback, PreheatingAdd deep setback slightly increase miss timeSave 34% energy, improve miss time by 51

mins

Effect of Each Component

Sensitivity to Number of SensorsAlmost negligibleSelected vs. All energy 28.9% vs. 23.6% miss time 54 mins v.s 48 minsSelected set performs better!!!???

Periodic vs. Aperiodic life styleSave more energy and miss less time for

periodic life style

Sensitivity to Occupancy Patterns

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

Sensitivity to Climate Zones

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 energyAlthough 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.

Conclusion

Q&A

Thanks~

Documents

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