HeatProbe
A Thermal-based Power Meter for
Accounting Disaggregated Electricity
Usage
Bo-Jhang Ho, Hsin-Liu (Cindy) Kao
Nan-Chen Chen, Chuang-Wen You, Hao-Hua Chu, Ming-Syan Chen
National Taiwan University, Academia Sinica
Outline
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Motivation
Approach
Design & implementation
Limitations
Evaluation
Related work
Conclusion
Outline
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Motivation
Approach
Design & implementation
Limitations
Evaluation
Related work
Conclusion
Energy issue
Unaware of per-appliance energy consumption
Single power meter at home
A master power meter
Measure per-appliance energy consumption
Too many meters
Outline
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Motivation
Approach
Design & implementation
Limitations
Evaluation
Related work
Conclusion
Thermal sensing approach
Master power meter
turn off
turn off
(W)
/通用格式
/通用格式
/通用格式
turn on
turn on
turn on
turn off
Thermal recording
• Increasing heat pattern -> appliance on
• Decreasing heat pattern -> appliance off
• Appliances generate heat as natural by-product of
operation.
x 32
HeatProbe system overview
(1) Heatmap segmentation
app #2
app #4
app #3
app #1
people
HeatProbe system overview
(1) Heatmap segmentation (2) Thermal event detection
。
( C)
/通用格式
app #2
app #4
on
off
/通用格式
/通用格式
/通用格式
app #3
app #1
/通用格式
(Min)
people
appliance
area #3
28.0 C
29.6 C
10 min
20 min
(10 min,
on,
app #3)
(20 min,
off,
app #3)
average temperature
time point
HeatProbe system overview
(1) Heatmap segmentation
app #2
app #4
app #3
app #1
people
(8 min, (18 min,
off,
on,
410 W) 410 W)
(W)
/通用格式
/通用格式
/通用格式
/通用格式
/通用格式
Power readings
(Min)
(3) Power event detection
HeatProbe system overview
(1) Heatmap segmentation (2) Thermal event detection
。
( C)
/通用格式
app #2
app #4
on
thermal
events
off
(on, app
#3,
10 min)
/通用格式
/通用格式
/通用格式
app #3
app #1
/通用格式
(Min)
(off, app #3,
20 min)
power
events
(on, 410 W,
8 min)
(off, 410 W,
18 min)
people
(on, app #4,
30 min)
on off on off
(W)
/通用格式
/通用格式
(off, app #4,
40 min)
/通用格式
(on, 50 W,
28 min)
(on, 50 W,
38 min)
/通用格式
/通用格式
Power readings
(Min)
(3) Power event detection (4) Matching
Outline
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Motivation
Approach
Limitations
Design & implementation
Evaluation
Related work
Conclusion
Limitation (1)
Appliance plugged into a metered circuit
Limitation (2)
Appliance surface within view of the thermal camera
Limitation (3)
System recognizes only appliance on/off binary state
Outline
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Motivation
Approach
Limitations
Design & implementation
Evaluation
Related work
Conclusion
(1) Heatmap segmentation
Human tracking
(1) Heatmap segmentation
Human tracking
foreground
background
(1) Heatmap segmentation
Appliance tracking
(2) Thermal event detection
(2) Thermal event detection
(2) Thermal event detection
(3) Power event detection
(4) Event matching/pairing
(W)
/通用格式
power
consumption
power
consumption
/通用格式
usage time
usage time
on
on
on
(。C)
/通用格式
/通用格式
/通用格式
/通用格式
off
on
off
off
off
time
Outline
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Motivation
Approach
Design & implementation
Limitations
Evaluation
Related work
Conclusion
Evaluation
3 scenarios : cubicle space, meeting room, and kitchen
Environment Cubicle spaces Meeting rooms Kitchen spaces
x2
x2
x2
x2
Appliances
x2
Participants
2 people
2 people
3 people
# of events
100
28
36
Result
Event detection accuracy, event time error
。
( C)
/通用格式
on
off
accuracy
precision
Average
detection
time error
Power
events
159 / 164
= 97%
97%
4.06 sec.
Thermal
events
159 / 164
= 97%
94%
112.25 sec.
Event detection
/通用格式
/通用格式
/通用格式
/通用格式
on off on off
(W)
/通用格式
/通用格式
/通用格式
/通用格式
/通用格式
Result
Average appliance usage time error = 125.03 sec
(80% events < 120 sec)
80%
120
Appliance usage time error (sec)
Result
Average matching accuracy = 77%
thermal power
events events
#1
on
#1
off
#2
on
#2
off
410 W
on
410 W
off
50 W
on
50 W
on
Item
Value
# event pairs
164
# of correctly
matched event pairs
126
Accuracy
77%
Result
Per-appliance power consumption error for cubic spaces
Power (W)
Ground-truth power consumption
Detected power consumption
7.94%
6.83%
43.88%
46.04%
37.97%
177.05%
1
2
1
2
1
2
11.78%
2.06%
14.24%
Result
Per-appliance power consumption error for meeting rooms
Power (W)
Ground-truth power consumption
Detected power consumption
8.77%
0.20%
1
2
2.45%
6.89%
1
0.33%
2
9.91%
1.45%
Result
Per-appliance power consumption error for meeting rooms
Power (W)
Ground-truth power consumption
Detected power consumption
Overall per-appliance power
consumption accuracy = 80.2%
8.77%
0.20%
1
2
2.45%
6.89%
1
0.33%
2
9.91%
1.45%
Outline
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•
Motivation
Approach
Design & implementation
Limitations
Evaluation
Related work
Conclusion
ViridiScope (Kim et al, UbiComp’09)
ElectriSense (Gupta et al, UbiComp’10)
Single plug-in
sensor module
Comparison to related work
Direct
Multi-point
sensing
Single-point
sensing
(per house)
Google power meter
NILM
Acme
(IPSN’09)
Electromagnetic
(or Magnetic)
Single-point
sensing
(per room)
(Energy and Buildings’96)
(IEEE’92)
Rowe et al.
(Proc. BuildSys’10)
ViridiScope
(UbiComp’09)
ElectriSense
EMI
Acoustics
Thermal
(UbiComp’10)
Fogarty et al.
Chen et al.
(UIST’06)
(Pervasive computing’05)
HeatProbe
Outline
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Motivation
Approach
Limitations
Design & implementation
Evaluation
Related work
Conclusion
Conclusion
• Explore a simple electro-thermal feature for
detecting per-appliance power consumption
• System achieves 80.2% accuracy
Thanks to reviewers & shepherd for valuable comments
A Thermal-based Power Meter for Accounting
Disaggregated Electricity Usage
HeatProbe
National Taiwan University, Academia Sinica
Bo-Jhang Ho, Hsin-Liu (Cindy) Kao, Nan-Chen Chen, Chuang-Wen You, Hao-Hua Chu, Ming-Syan Chen