25. • central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSense
26. • central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSense
BelkinEcho
27. • central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSense
40#
50#
60#
70#
80#
Cold Line Pressure
(Hose Spigot)
0 94.5
time (s)
psi
BelkinEcho
28. • central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSense
40#
50#
60#
70#
80#
Cold Line Pressure
(Hose Spigot)
0 94.5
time (s)
psi
BelkinEcho
29. • central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSense
40#
50#
60#
70#
80#
Cold Line Pressure
(Hose Spigot)
0 94.5
time (s)
psi
open close
BelkinEcho
37. feasibility study
Froehlich, J., Larson, E., Campbell, T., Haggerty, C., Fogarty, J., and Patel, S.N. HydroSense: infrastructure-mediated single-point sensing of whole-home water
activity. Proceedings of the 11th ACM international conference on Ubiquitous computing, (2009), 235–244.
Larson, E., Froehlich, J., Campbell, T., et al. Disaggregated water sensing from a single, pressure- based sensor: An extended analysis of HydroSense using staged
experiments. Pervasive and Mobile Computing, (2010).
38. feasibility study
• 10 homes
Froehlich, J., Larson, E., Campbell, T., Haggerty, C., Fogarty, J., and Patel, S.N. HydroSense: infrastructure-mediated single-point sensing of whole-home water
activity. Proceedings of the 11th ACM international conference on Ubiquitous computing, (2009), 235–244.
Larson, E., Froehlich, J., Campbell, T., et al. Disaggregated water sensing from a single, pressure- based sensor: An extended analysis of HydroSense using staged
experiments. Pervasive and Mobile Computing, (2010).
39. feasibility study
• 10 homes
• staged calibration
Froehlich, J., Larson, E., Campbell, T., Haggerty, C., Fogarty, J., and Patel, S.N. HydroSense: infrastructure-mediated single-point sensing of whole-home water
activity. Proceedings of the 11th ACM international conference on Ubiquitous computing, (2009), 235–244.
Larson, E., Froehlich, J., Campbell, T., et al. Disaggregated water sensing from a single, pressure- based sensor: An extended analysis of HydroSense using staged
experiments. Pervasive and Mobile Computing, (2010).
40. feasibility study
• 10 homes
• staged calibration
• ~98% accuracy
Froehlich, J., Larson, E., Campbell, T., Haggerty, C., Fogarty, J., and Patel, S.N. HydroSense: infrastructure-mediated single-point sensing of whole-home water
activity. Proceedings of the 11th ACM international conference on Ubiquitous computing, (2009), 235–244.
Larson, E., Froehlich, J., Campbell, T., et al. Disaggregated water sensing from a single, pressure- based sensor: An extended analysis of HydroSense using staged
experiments. Pervasive and Mobile Computing, (2010).
51. totals
days
33
33
30
27
33
156
events
2374
3075
4754
2499
2578
14,960
events/day
71.9
93.2
158.5
92.6
78.1
95.9
compound
22.2%
21.8%
16.6%
32%
21.3%
21.9%
data collection
Larson, E., Froehlich, J., Saba, E., et al. A Longitudinal Study of Pressure Sensing to Infer Real- World Water Usage
Events in the Home. Pervasive Computing, Springer (2011), 50–69.
52. totals
days
33
33
30
27
33
156
events
2374
3075
4754
2499
2578
14,960
events/day
71.9
93.2
158.5
92.6
78.1
95.9
compound
22.2%
21.8%
16.6%
32%
21.3%
21.9%
data collection
Larson, E., Froehlich, J., Saba, E., et al. A Longitudinal Study of Pressure Sensing to Infer Real- World Water Usage
Events in the Home. Pervasive Computing, Springer (2011), 50–69.
most comprehensive labeled dataset
of hot and cold water ever collected
67. machine learning overview
minimal set of labels for calibrationlabeled
RF classifier
predicted fixture
one or two examples per fixture
accuracy 40-60%
68. machine learning overview
minimal set of labels for calibrationlabeled
RF classifier
predicted fixture
one or two examples per fixture
accuracy 40-60%
need more labels!
69. machine learning overview
minimal set of labels for calibrationlabeled
RF classifier
predicted fixture
one or two examples per fixture
accuracy 40-60%
need more labels!
but which ones?
77. active learning
leveraging the homeowner
• select low confidence margin examples
• ask homeowner for label
AT&T LTEAT&T LTE 5:23 PM
did you just use water?
YesNo
78. active learning
leveraging the homeowner
• select low confidence margin examples
• ask homeowner for label
AT&T LTEAT&T LTE 5:23 PM
did you just use water?
YesNo
AT&T LTEAT&T LTE 5:23 PM
Fixture Selection
Do Not Disturb
Select Notification Times
OFF
Master Toilet
Recently Used:
Half Bath Toilet
Dishwasher
Master Sink Select Temp.
Kitchen Sink Select Temp.
Master Shower Select Temp.
More
Half Bath Sink Select Temp.
79. simulating labels from homeowner
AT&T LTEAT&T LTE 5:23 PM
did you just use water?
YesNo
AT&T LTEAT&T LTE 5:23 PM
Fixture Selection
Do Not Disturb
Select Notification Times
OFF
Master Toilet
Recently Used:
Half Bath Toilet
Dishwasher
Master Sink Select Temp.
Kitchen Sink Select Temp.
Master Shower Select Temp.
More
Half Bath Sink Select Temp.
80. simulating labels from homeowner
AT&T LTEAT&T LTE 5:23 PM
did you just use water?
YesNo
• ask for two labels every other day
AT&T LTEAT&T LTE 5:23 PM
Fixture Selection
Do Not Disturb
Select Notification Times
OFF
Master Toilet
Recently Used:
Half Bath Toilet
Dishwasher
Master Sink Select Temp.
Kitchen Sink Select Temp.
Master Shower Select Temp.
More
Half Bath Sink Select Temp.
81. simulating labels from homeowner
AT&T LTEAT&T LTE 5:23 PM
did you just use water?
YesNo
• ask for two labels every other day
• one morning and one evening
AT&T LTEAT&T LTE 5:23 PM
Fixture Selection
Do Not Disturb
Select Notification Times
OFF
Master Toilet
Recently Used:
Half Bath Toilet
Dishwasher
Master Sink Select Temp.
Kitchen Sink Select Temp.
Master Shower Select Temp.
More
Half Bath Sink Select Temp.
82. simulating labels from homeowner
AT&T LTEAT&T LTE 5:23 PM
did you just use water?
YesNo
• ask for two labels every other day
• one morning and one evening
• only from 8AM-9PM
AT&T LTEAT&T LTE 5:23 PM
Fixture Selection
Do Not Disturb
Select Notification Times
OFF
Master Toilet
Recently Used:
Half Bath Toilet
Dishwasher
Master Sink Select Temp.
Kitchen Sink Select Temp.
Master Shower Select Temp.
More
Half Bath Sink Select Temp.
83. 10 15 20 25 30 35 40 45
0.65
0.7
0.75
0.8
0.85
Co−Labeling in H1
Number of Labels
ValveLevelAccuracyofCoLabel−HMM
Co−Labeling
Random Labeling
simulating labels from homeowner
active learning for H1 totals
days
33
33
30
27
33
156
events
2374
3075
4754
2499
2578
14,960
events/day
71.9
93.2
158.5
92.6
78.1
95.9
compound
22.2%
21.8%
16.6%
32%
21.3%
21.9%
fixturepredictionaccuracy
number of labeled examples
84. 10 15 20 25 30 35 40 45
0.65
0.7
0.75
0.8
0.85
Co−Labeling in H1
Number of Labels
ValveLevelAccuracyofCoLabel−HMM
Co−Labeling
Random Labeling
simulating labels from homeowner
active learning for H1minimaltrainingset totals
days
33
33
30
27
33
156
events
2374
3075
4754
2499
2578
14,960
events/day
71.9
93.2
158.5
92.6
78.1
95.9
compound
22.2%
21.8%
16.6%
32%
21.3%
21.9%
fixturepredictionaccuracy
number of labeled examples
85. 10 15 20 25 30 35 40 45
0.65
0.7
0.75
0.8
0.85
Co−Labeling in H1
Number of Labels
ValveLevelAccuracyofCoLabel−HMM
Co−Labeling
Random Labeling
simulating labels from homeowner
active learning for H1minimaltrainingset totals
days
33
33
30
27
33
156
events
2374
3075
4754
2499
2578
14,960
events/day
71.9
93.2
158.5
92.6
78.1
95.9
compound
22.2%
21.8%
16.6%
32%
21.3%
21.9%
fixturepredictionaccuracy
number of labeled examples
86. 10 15 20 25 30 35 40 45
0.65
0.7
0.75
0.8
0.85
Co−Labeling in H1
Number of Labels
ValveLevelAccuracyofCoLabel−HMM
Co−Labeling
Random Labeling
chosen labels
random labeling
iteration 1
iteration 3
iteration 5
iteration 10
simulating labels from homeowner
active learning for H1minimaltrainingset totals
days
33
33
30
27
33
156
events
2374
3075
4754
2499
2578
14,960
events/day
71.9
93.2
158.5
92.6
78.1
95.9
compound
22.2%
21.8%
16.6%
32%
21.3%
21.9%
fixturepredictionaccuracy
number of labeled examples
95. implications for NILM
AT&T LTEAT&T LTE 5:23 PM
Fixture Selection
Do Not Disturb
Select Notification Times
OFF
Master Toilet
Recently Used:
Half Bath Toilet
Dishwasher
Master Sink Select Temp.
Kitchen Sink Select Temp.
Master Shower Select Temp.
More
Half Bath Sink Select Temp.
96. implications for NILM
AT&T LTEAT&T LTE 5:23 PM
Fixture Selection
Do Not Disturb
Select Notification Times
OFF
Master Toilet
Recently Used:
Half Bath Toilet
Dishwasher
Master Sink Select Temp.
Kitchen Sink Select Temp.
Master Shower Select Temp.
More
Half Bath Sink Select Temp.
did you plug in laptop?
97. implications for NILM
AT&T LTEAT&T LTE 5:23 PM
Fixture Selection
Do Not Disturb
Select Notification Times
OFF
Master Toilet
Recently Used:
Half Bath Toilet
Dishwasher
Master Sink Select Temp.
Kitchen Sink Select Temp.
Master Shower Select Temp.
More
Half Bath Sink Select Temp.
did you plug in laptop?
did you recently start the oven?
98. implications for NILM
AT&T LTEAT&T LTE 5:23 PM
Fixture Selection
Do Not Disturb
Select Notification Times
OFF
Master Toilet
Recently Used:
Half Bath Toilet
Dishwasher
Master Sink Select Temp.
Kitchen Sink Select Temp.
Master Shower Select Temp.
More
Half Bath Sink Select Temp.
did you plug in laptop?
did you recently start the oven?
are you watching TV?
99. implications for NILM
AT&T LTEAT&T LTE 5:23 PM
Fixture Selection
Do Not Disturb
Select Notification Times
OFF
Master Toilet
Recently Used:
Half Bath Toilet
Dishwasher
Master Sink Select Temp.
Kitchen Sink Select Temp.
Master Shower Select Temp.
More
Half Bath Sink Select Temp.
did you plug in laptop?
did you recently start the oven?
are you watching TV?
-can potentially start using more
home specific features
103. limitations
responsive cloud architecture
multiple people in a home
trust in user as “oracle”
AT&T LTEAT&T LTE 5:23 PM
Fixture Selection
Do Not Disturb
Select Notification Times
OFF
Master Toilet
Recently Used:
Half Bath Toilet
Dishwasher
Master Sink Select Temp.
Kitchen Sink Select Temp.
Master Shower Select Temp.
More
Half Bath Sink Select Temp.
104. consumer centered calibration in end-use
water monitoring
eric c. larson | eclarson.com
Assistant Professor Computer Science and Engineering
Thank You!
eclarson.com
eclarson@lyle.smu.edu
@ec_larson
acknowledgements
Belkin, Inc.
Shwetak Patel
Jon Froehlich
Sidhant Gupta
Les Atlas