Consumer Centered Calibration End Use Water Monitoring
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Transcript of Consumer Centered Calibration End Use Water Monitoring
consumer centered calibration in end-use water monitoring
eric c. larson | eclarson.comAssistant Professor Computer Science and Engineering
on average, a consumer can save 15-25%
lake mead 1983
lake mead 2011
we are using water faster than it is being replenished
Pacific Institute for Studies in Development, Environment, and Security, 2011
we are using water faster than it is being replenished
Pacific Institute for Studies in Development, Environment, and Security, 2011
$2,994.83
more awareness
Fabricated"Unit
16
Water"Meter"Design
15
TurbineInsert
Thermistor
Flow
image: LBNL
Fabricated"Unit
16
Water"Meter"Design
15
TurbineInsert
Thermistor
Flow
image: LBNL
Fabricated"Unit
16
Water"Meter"Design
15
TurbineInsert
Thermistor
Flow
image: LBNL
metersflow rate fixture flowinline waterFabricated"Unit
16
metersflow rate fixture flowinline water
water pressure
pressure sensor
Fabricated"Unit
16
metersflow rate fixture flowinline water
water pressure
pressure sensor
Fabricated"Unit
16
metersflow rate fixture flowinline water
water pressure
pressure sensor
machine learning
estimated
Fabricated"Unit
16
HydroSense
• central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSense
• central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSenseBelkinEcho
• central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSense
40#
50#
60#
70#
80#
Cold Line Pressure (Hose Spigot)
0 9 4.5
time (s)
psi
BelkinEcho
• central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSense
40#
50#
60#
70#
80#
Cold Line Pressure (Hose Spigot)
0 9 4.5
time (s)
psi
BelkinEcho
• central sensing point
• easy to install
• low cost
• can observe every fixture
HydroSense
40#
50#
60#
70#
80#
Cold Line Pressure (Hose Spigot)
0 9 4.5
time (s)
psi
open close
BelkinEcho
kitchen sink
upstairs toilet
downstairs toilet
kitchen sink
upstairs toilet
downstairs toilettotals
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%
= 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%
kitchen sink
upstairs toilet
template matching
unknown eventdownstairs toilet
kitchen sink
upstairs toilet
template matching
unknown event
downstairs toilet
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).
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).
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).
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).
70
50
30
pres
sure
(psi)
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50
30
pres
sure
(psi)
initial study: staged events
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
initial study: staged events
kitchen sink kitchen sink
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
natural water use
how well does single point sensing work in a natural setting?
longitudinal evaluation
longitudinal evaluation
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.
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
natural water usage
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
natural water usage
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
8AM 2 minutes
bathroom sink
natural water usage
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
8AM 2 minutes
toilet
bathroom sink
natural water usage
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
8AM 2 minutes
kitchen sink kitchen sink
toilet
bathroom sink
natural water usage
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
8AM 2 minutes
kitchen sink kitchen sink
toilet
bathroom sink
natural water usage
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
template matching: 98% 74%
8AM 2 minutes
kitchen sink kitchen sink
toilet
bathroom sink
natural water usage
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
template matching: 98% 74%10 fold cross validation
8AM 2 minutes
kitchen sink kitchen sink
toilet
bathroom sink
natural water usage
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
template matching: 98% 74%10 fold cross validation
35%
8AM 2 minutes
minimal
two step process: segmentation70
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30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
standard deviation smoothing operation
pressure deltas local maxima (for adjustments/compound)
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
pressure drop min pressure
duration min local maxima
max derivative max amplitude
LPC frequency characteristics sink flow meter
two step process: features extraction
x
s1
70
50
30
pres
sure
(psi)
70
50
30
pres
sure
(psi)
pressure drop min pressure
duration min local maxima
max derivative max amplitude
LPC frequency characteristics sink flow meter
two step process: features extraction
x
s1
x
d2
x
d3
x
d4
x
d5x
d6
x
d7
machine learning overview
labeled
RF classifier
predicted fixture
machine learning overviewminimal set of labels for calibrationlabeled
RF classifier
predicted fixture
machine learning overviewminimal set of labels for calibrationlabeled
RF classifier
predicted fixture
one or two examples per fixture
machine learning overviewminimal set of labels for calibrationlabeled
RF classifier
predicted fixture
one or two examples per fixture
accuracy 40-60%
machine learning overviewminimal set of labels for calibrationlabeled
RF classifier
predicted fixture
one or two examples per fixture
accuracy 40-60%
need more labels!
machine learning overviewminimal set of labels for calibrationlabeled
RF classifier
predicted fixture
one or two examples per fixture
accuracy 40-60%
need more labels!but which ones?
active learning
labeled
classifier 1 classifier N…
rando
m for
est
active learning
labeled
classifier 1 classifier N
unlabeled
…
rando
m for
est
active learning
labeled
classifier 1 classifier N
unlabeled
disagree?
…
rando
m for
est
active learning
labeled
classifier 1 classifier N
unlabeled
disagree?
max margin of committee
…
rando
m for
est
need a labelmargin < 20%
active learning leveraging the homeowner
active learning leveraging the homeowner
• select low confidence margin examples
active learning leveraging the homeowner
• select low confidence margin examples
• ask homeowner for label
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
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.
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.
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.
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 eveningAT&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.
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-9PMAT&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.
10 15 20 25 30 35 40 45
0.65
0.7
0.75
0.8
0.85Co−Labeling in H1
Number of Labels
Valv
e Le
vel A
ccur
acy
of C
oLab
el−H
MM
Co−LabelingRandom Labeling
simulating labels from homeowneractive 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%
fixtu
re p
redi
ctio
n ac
cura
cy
number of labeled examples
10 15 20 25 30 35 40 45
0.65
0.7
0.75
0.8
0.85Co−Labeling in H1
Number of Labels
Valv
e Le
vel A
ccur
acy
of C
oLab
el−H
MM
Co−LabelingRandom Labeling
simulating labels from homeowneractive learning for H1
mini
mal
traini
ng s
ettotals
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%
fixtu
re p
redi
ctio
n ac
cura
cy
number of labeled examples
10 15 20 25 30 35 40 45
0.65
0.7
0.75
0.8
0.85Co−Labeling in H1
Number of Labels
Valv
e Le
vel A
ccur
acy
of C
oLab
el−H
MM
Co−LabelingRandom Labeling
simulating labels from homeowneractive learning for H1
mini
mal
traini
ng s
ettotals
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%
fixtu
re p
redi
ctio
n ac
cura
cy
number of labeled examples
10 15 20 25 30 35 40 45
0.65
0.7
0.75
0.8
0.85Co−Labeling in H1
Number of Labels
Valv
e Le
vel A
ccur
acy
of C
oLab
el−H
MM
Co−LabelingRandom Labelingchosen labels random labeling
iteration 1
iteration 3
iteration 5
iteration 10
simulating labels from homeowneractive learning for H1
mini
mal
traini
ng s
ettotals
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%
fixtu
re p
redi
ctio
n ac
cura
cy
number of labeled examples
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%
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%
60%
70%
80%
90%
100%
0 1 2 3 4 5 6 7 8 9 10 11 12 13
valve fixture category
error bars=std err.
active learning iteration
accu
racy
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%
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%
60%
70%
80%
90%
100%
0 1 2 3 4 5 6 7 8 9 10 11 12 13
valve fixture category
error bars=std err.
active learning iteration
accu
racy
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%
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%
60%
70%
80%
90%
100%
0 1 2 3 4 5 6 7 8 9 10 11 12 13
valve fixture category
error bars=std err.
active learning iteration
accu
racy
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%
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%
60%
70%
80%
90%
100%
0 1 2 3 4 5 6 7 8 9 10 11 12 13
valve fixture category
error bars=std err.
active learning iteration
accu
racy
133
followed by the toilet. This advocates the importance of using the DBN-VE as a baseline classifier,
because co-labeling only marginally increases the diversity of class examples. Even so, a shower example
is typically asked for in the first two iterations, but washing machines and dishwashers are not asked for
until typically the fifth or sixth iteration. This is not a problem, however, because the rule based classifier
and DBN-VE are able to leverage prior knowledge in classifying these fixtures and appliances.
A final investigation of the confusions reveals that the system, after 10 iterations of co-labeling, is
highly accurate among each fixture, although temperature confusions still exist (Figure 8-20). The most
common confusion is the secondary bathroom shower for the master bathroom shower.
Figure 8-20. The final confusion matrix for the CoLabel-DBN algorithm
For comparison to the other algorithms, I also show the improvement in the across fixture accuracy at
the valve, lumped fixture, and fixture category level, shown in Figure 8-21.
-32974
8.5
1.5
-328
6.1
72
1.1
0.8
5.0
0.7
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0.6
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18
92
0.6
5.4
2.6
4.9
3.4
2.3
3.5
0.9
5.1
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18
0.9
92
1.4
4.9
0.7
4.4
1.4
1.4
7.3
1.0
0.7
3.2
-72053
2.1 -1651
4.2
67
0.6
2.4
0.8
31
2.5
5.2
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1.2
5.0
0.6
18
4.2
88
1.8
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1.5
1.6
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1.0
-5430
1.2
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5.3
4.6
10
1.8
89
1.7
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1.4
0.7
3.2
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2.9
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81
12
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94
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8.1
-133
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68
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2.8
42
-1628
1.9
2.9
0.5
25
0.8
95
0.7 -1625
2.2
3.1
10
1.3
96
6.6 -27290
-316
1.1
0.7
59
18 -308
0.9
0.9
15
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Dishwasher close,1
open,2
KitchenSink close,3
open,4
M.BathroomShower close,5
open,6
M.BathroomSink close,7
open,8
M.BathroomToilet close,9
open,10
S.BathroomShower close,11
open,12
S.BathroomSink close,13
open,14
S.BathroomToilet open,15
WashingMachine close,16
open,17
133
followed by the toilet. This advocates the importance of using the DBN-VE as a baseline classifier,
because co-labeling only marginally increases the diversity of class examples. Even so, a shower example
is typically asked for in the first two iterations, but washing machines and dishwashers are not asked for
until typically the fifth or sixth iteration. This is not a problem, however, because the rule based classifier
and DBN-VE are able to leverage prior knowledge in classifying these fixtures and appliances.
A final investigation of the confusions reveals that the system, after 10 iterations of co-labeling, is
highly accurate among each fixture, although temperature confusions still exist (Figure 8-20). The most
common confusion is the secondary bathroom shower for the master bathroom shower.
Figure 8-20. The final confusion matrix for the CoLabel-DBN algorithm
For comparison to the other algorithms, I also show the improvement in the across fixture accuracy at
the valve, lumped fixture, and fixture category level, shown in Figure 8-21.
-32974
8.5
1.5
-328
6.1
72
1.1
0.8
5.0
0.7
1.5
0.6
-7180
18
92
0.6
5.4
2.6
4.9
3.4
2.3
3.5
0.9
5.1
-7211
18
0.9
92
1.4
4.9
0.7
4.4
1.4
1.4
7.3
1.0
0.7
3.2
-72053
2.1 -1651
4.2
67
0.6
2.4
0.8
31
2.5
5.2
-5253
1.2
5.0
0.6
18
4.2
88
1.8
7.5
1.5
1.6
5.7
1.0
-5430
1.2
0.5
5.3
4.6
10
1.8
89
1.7
1.7
1.4
0.7
3.2
-2398
2.9
0.6
81
12
-2433
0.8
94
0.6
8.1
-133
6.7
68
-259
2.8
42
-1628
1.9
2.9
0.5
25
0.8
95
0.7 -1625
2.2
3.1
10
1.3
96
6.6 -27290
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1.1
0.7
59
18 -308
0.9
0.9
15
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Dishwasher close,1
open,2
KitchenSink close,3
open,4
M.BathroomShower close,5
open,6
M.BathroomSink close,7
open,8
M.BathroomToilet close,9
open,10
S.BathroomShower close,11
open,12
S.BathroomSink close,13
open,14
S.BathroomToilet open,15
WashingMachine close,16
open,17
dishwasher laundry
dishwasher
laundry
133
followed by the toilet. This advocates the importance of using the DBN-VE as a baseline classifier,
because co-labeling only marginally increases the diversity of class examples. Even so, a shower example
is typically asked for in the first two iterations, but washing machines and dishwashers are not asked for
until typically the fifth or sixth iteration. This is not a problem, however, because the rule based classifier
and DBN-VE are able to leverage prior knowledge in classifying these fixtures and appliances.
A final investigation of the confusions reveals that the system, after 10 iterations of co-labeling, is
highly accurate among each fixture, although temperature confusions still exist (Figure 8-20). The most
common confusion is the secondary bathroom shower for the master bathroom shower.
Figure 8-20. The final confusion matrix for the CoLabel-DBN algorithm
For comparison to the other algorithms, I also show the improvement in the across fixture accuracy at
the valve, lumped fixture, and fixture category level, shown in Figure 8-21.
-32974
8.5
1.5
-328
6.1
72
1.1
0.8
5.0
0.7
1.5
0.6
-7180
18
92
0.6
5.4
2.6
4.9
3.4
2.3
3.5
0.9
5.1
-7211
18
0.9
92
1.4
4.9
0.7
4.4
1.4
1.4
7.3
1.0
0.7
3.2
-72053
2.1 -1651
4.2
67
0.6
2.4
0.8
31
2.5
5.2
-5253
1.2
5.0
0.6
18
4.2
88
1.8
7.5
1.5
1.6
5.7
1.0
-5430
1.2
0.5
5.3
4.6
10
1.8
89
1.7
1.7
1.4
0.7
3.2
-2398
2.9
0.6
81
12
-2433
0.8
94
0.6
8.1
-133
6.7
68
-259
2.8
42
-1628
1.9
2.9
0.5
25
0.8
95
0.7 -1625
2.2
3.1
10
1.3
96
6.6 -27290
-316
1.1
0.7
59
18 -308
0.9
0.9
15
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Dishwasher close,1
open,2
KitchenSink close,3
open,4
M.BathroomShower close,5
open,6
M.BathroomSink close,7
open,8
M.BathroomToilet close,9
open,10
S.BathroomShower close,11
open,12
S.BathroomSink close,13
open,14
S.BathroomToilet open,15
WashingMachine close,16
open,17
dishwasher laundry
dishwasher
laundry
implications for NILM
implications for NILMAT&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.
implications for NILMAT&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?
implications for NILMAT&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?
implications for NILMAT&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?
implications for NILMAT&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
limitations
limitationsresponsive cloud architecture
limitationsresponsive cloud architecturemultiple people in a home
limitationsresponsive cloud architecturemultiple people in a hometrust 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.
consumer centered calibration in end-use water monitoring
eric c. larson | eclarson.comAssistant Professor Computer Science and Engineering
Thank You!
eclarson.com [email protected] @ec_larson
acknowledgements Belkin, Inc.
Shwetak Patel Jon Froehlich Sidhant Gupta
Les Atlas