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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
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Motivation 43%
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State of the Art Too much cost! $5,000 - $25,000
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State of the Art Too much hassle! Too much hassle! User discomfort
Energy waste 55 60 65 70 75 Temperature (oF) Setpoint Setpoint Setback Home Home Home Home 00:00 24:00 08:00 18:00
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“How much energy can be saved with occupancy sensors?”
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Using Occupancy Sensors
55 60 65 70 75 Temperature (oF) Home Home Home Home 00:00 24:00 08:00 18:00
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The Wrong Way “Reactive” Thermostat Increase energy usage!
55 60 65 70 75 Temperature (oF) Slow Reaction Shallow Setback Inefficient Reaction Home Home 00:00 24:00 08:00 18:00
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Our Approach Smart Thermostat Automatically save energy! Fast reaction
55 60 65 70 75 Temperature (oF) Fast reaction Deep setback Preheating Home Home 00:00 24:00 08:00 18:00 Automatically save energy!
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Rest of the talk System Design Evaluation Fast Reaction Preheating
Deep Setback Evaluation
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1. Fast Reaction “Reactive" Thermostat Inactivity detector
Active/Inactive User discomfort Energy waste 55 60 65 70 75 Temperature (oF) Home Home 00:00 24:00 08:00 18:00
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Without increasing false positives
1. Fast Reaction Smart Thermostat Pattern detector Active/Away/Asleep 55 60 65 70 75 Temperature (oF) Detect within minutes Without increasing false positives Home Home 00:00 24:00 08:00 18:00
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2. Preheating “Why preheat?” Preheat – slow but efficient
Heat pump React – fast but inefficient Electric coils Gas furnace How to decide when to preheat? Energy waste Energy waste 55 60 65 70 75 Temperature (oF) Home Home 00:00 24:00 08:00 18:00
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2. Preheating Preheat React Optimal Preheat Time
Arrival Time Distribution 16:00 18:00 20:00 Preheat React Optimal Preheat Time Expected Energy Usage (kWh) 3 2 1 16:00 18:00 20:00 Time
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Arrival Time Distribution
3. Deep Setback Arrival Time Distribution 16:00 18:00 20:00 Earliest expected arrival time Optimal preheat time Shallow setback 55 60 65 70 75 Temperature (oF) Deep setback ?? Home Home 00:00 24:00 08:00 18:00
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Rest of the talk System Design Evaluation Fast Reaction Preheating
Deep Setback Evaluation
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Evaluation Occupancy Data Energy Measurements EnergyPlus Simulator
Home #Residents # Motion Sensors #Door A 1 7 3 B 2 C 4 D E 5 F G H EnergyPlus Simulator
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Energy Savings Optimal Reactive Smart Optimal: 35.9% Smart: 28.8%
B C D E F G H Energy Savings (%) -10 10 20 30 40 50 60 Home Deployments Optimal Reactive Smart Optimal: 35.9% Smart: 28.8% Reactive: 6.8%
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Average Daily Miss Time (min)
User Comfort 80 A B C D E F G H Average Daily Miss Time (min) 40 20 60 100 120 Home Deployments Reactive Smart Reactive: 60 min Smart: 48 min
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Generalization Person Types House Types Climate Zones Zone 1
Minneapolis, MN Zone 2 Pittsburg, PA Zone 3 Washington, D.C. Zone 4 San Francisco, CA Zone 5 Houston, TX
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Impact Nationwide Savings “Bang for the buck”
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 Thank you!
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