The Smart Thermostat: Using Occupancy Sensors to Save Energy in Homes Jiakang Lu, Tamim Sookoor,...

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The Smart Thermostat: Using Occupancy Sensors to Save Energy in Homes Jiakang Lu, Tamim Sookoor, Vijay Srinivasan, Ge Gao, Brian Holben, John Stankovic, Eric Field, Kamin Whitehouse SenSys 2010 Zurich, Switzerland

Transcript of The Smart Thermostat: Using Occupancy Sensors to Save Energy in Homes Jiakang Lu, Tamim Sookoor,...

The Smart Thermostat: Using Occupancy Sensors to Save Energy in Homes

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

SenSys 2010Zurich, Switzerland

Motivation

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43%

State of the Art

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$5,000 - $25,000Too much cost!

00:00 24:0008:00 18:00

Home

State of the Art

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55

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65

70

75

Te

mp

era

ture

(oF

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Too much hassle! Too much hassle!

Home Home

Setpoint Setpoint

Setback

Home

User discomfort

Energy waste

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“How much energy can be savedwith occupancy sensors?”

Using Occupancy Sensors

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HomeHome HomeHome

“Reactive” Thermostat

The Wrong Way

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mp

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ture

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Home Home

Shallow Setback

Increase energy usage!

Slow Reaction Inefficient Reaction

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Our Approach Smart Thermostat

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Home Home

Fast reaction Deep setback Preheating

Automatically save energy!

Rest of the talk System Design

Fast Reaction Preheating Deep Setback

Evaluation

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1. Fast Reaction “Reactive" Thermostat

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Home Home

Inactivity detector

Active/Inactive

User discomfort

Energy waste

1. Fast Reaction Smart Thermostat

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Pattern detector

Active/Away/Asleep

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Home Home

Detect within minutesWithout increasing false positives

2. Preheating“Why preheat?” Preheat – slow but efficient

Heat pump

React – fast but inefficient Electric coils Gas furnace

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00:00 24:0008:00 18:00

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Home Home

Energy wasteEnergy waste

How to decide when to preheat?

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PreheatReact

Arrival Time Distribution

2. Preheating

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Exp

ecte

d E

nerg

y U

sage

(k

Wh)

3

2

1

016:00 18:00 20:00

Time

Optimal Preheat

Time

16:00 18:00 20:00

3. Deep Setback

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Arrival Time Distribution

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HomeHome55

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Earliest expectedarrival time

Optimal preheat time

Deep setback

Shallow setback

??

Rest of the talk System Design

Fast Reaction Preheating Deep Setback

Evaluation

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Home #Residents # MotionSensors

#DoorSensors

A 1 7 3

B 1 3 2

C 1 4 1

D 1 4 1

E 2 5 1

F 3 5 2

G 3 4 1

H 2 5 2

EnergyPlus Simulator

Evaluation Occupancy Data

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Energy Measurements

Energy Savings

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OptimalReactiveSmart

Smart: 28.8%

Reactive: 6.8%

A B C D E F G H

En

erg

y S

avin

gs

(%)

-10

0

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Home Deployments

Optimal: 35.9%

User Comfort

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ReactiveSmart

Smart: 48 min

Reactive: 60 min

80

A B C D E F G H0

Ave

rag

e D

aily

Mis

s T

ime

(min

)

40

20

60

100

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Home Deployments

Person Types

Generalization

House Types

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Zone 1 Minneapolis, MN

Zone 2 Pittsburg, PA

Zone 3 Washington, D.C.

Zone 4 San Francisco, CA

Zone 5 Houston, TX

Climate Zones

Impact Nationwide Savings

save over 100 billion kWh per year prevent 1.12 billion tons of air pollutants

“Bang for the buck” $5 billion for weatherization Our technique is ~$25 in sensors per home

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Conclusions Three simple techniques, but able to achieve

large savings: 28% on average low cost: $25 in sensors per home low hassle: automatic temperature control

Promising sensing-based solution

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Q & A

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Thank you!