Upload
gunda
View
25
Download
0
Tags:
Embed Size (px)
DESCRIPTION
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. Motivation. 43%. State of the Art. Too much cost!. $5,000 - $25,000. - PowerPoint PPT Presentation
Citation preview
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
2
43%
State of the Art
3
$5,000 - $25,000Too much cost!
00:00 24:0008:00 18:00
Home
State of the Art
4
55
60
65
70
75
Te
mp
era
ture
(oF
)
Too much hassle! Too much hassle!
Home Home
Setpoint Setpoint
Setback
Home
User discomfort
Energy waste
5
“How much energy can be savedwith occupancy sensors?”
Using Occupancy Sensors
6
00:00 24:0008:00 18:00
55
60
65
70
75
Te
mp
era
ture
(oF
)
HomeHome HomeHome
“Reactive” Thermostat
The Wrong Way
7
00:00 24:0008:00 18:00
55
60
65
70
75
Te
mp
era
ture
(oF
)
Home Home
Shallow Setback
Increase energy usage!
Slow Reaction Inefficient Reaction
00:00 24:0008:00 18:00
Our Approach Smart Thermostat
8
55
60
65
70
75
Te
mp
era
ture
(oF
)
Home Home
Fast reaction Deep setback Preheating
Automatically save energy!
Rest of the talk System Design
Fast Reaction Preheating Deep Setback
Evaluation
9
1. Fast Reaction “Reactive" Thermostat
10
00:00 24:0008:00 18:00
55
60
65
70
75
Te
mp
era
ture
(oF
)
Home Home
Inactivity detector
Active/Inactive
User discomfort
Energy waste
1. Fast Reaction Smart Thermostat
11
Pattern detector
Active/Away/Asleep
00:00 24:0008:00 18:00
55
60
65
70
75
Te
mp
era
ture
(oF
)
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
12
00:00 24:0008:00 18:00
55
60
65
70
75
Te
mp
era
ture
(oF
)
Home Home
Energy wasteEnergy waste
How to decide when to preheat?
16:00 18:00 20:00
PreheatReact
Arrival Time Distribution
2. Preheating
13
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
14
Arrival Time Distribution
00:00 24:0008:00 18:00
HomeHome55
60
65
70
75
Te
mp
era
ture
(oF
)
Earliest expectedarrival time
Optimal preheat time
Deep setback
Shallow setback
??
Rest of the talk System Design
Fast Reaction Preheating Deep Setback
Evaluation
15
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
16
Energy Measurements
Energy Savings
17
OptimalReactiveSmart
Smart: 28.8%
Reactive: 6.8%
A B C D E F G H
En
erg
y S
avin
gs
(%)
-10
0
10
20
30
40
50
60
Home Deployments
Optimal: 35.9%
User Comfort
18
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
120
Home Deployments
Person Types
Generalization
House Types
19
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
20
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
21
Q & A
22
Thank you!