Sensing for Sustainability: Disaggregated Sensing of Electricity, Gas, and Water

Sensing for Sustainability
disaggregated sensing of water, electricity, and natural gas
University of Washington
Eric Larson
PhD Candidate in School of Electrical and Computer Engineering
UbiComp Lab
electrical
engineering
computer
science
presented at
December 7th, 2012
Saturday, December 8, 12

per capita electricity usage in U.S. ~39.3 kWh/p/delectricity

per capita electricity usage in U.S. ~39.3 kWh/p/d
2 times Japan
Germany
1.5 times Taiwan
France
UK
electricity

its not just electricity

smarter infrastructure
consumer awareness
informed public policy

Pool Pump
31%

Pool Pump
31%
DVRs
11%

toilet
78.4 gallons
shower
62.4 gallons
bath
6.5 gallons
today‘s usage
bathroom sink 1
4.2 gallons
bathroom sink 2
0.8 gallons

sor Shwetak N. Patel Computer Science
& Engineering
Electrical
Engineering
design + use +
build
ElectriSense InGen
SNUPI
ense
mart
GasSense
uToSNUPI
GasSense
HydroSense
ElderCare
Mobility
Rehabilitation
HydroSense

Professor Shwetak N. Patel Computer Science
& Engineering
Ele
Engi
ElectriSense
CoughSense
SpiroSmart
ElderCare
GasSense
HydroSense

regulator

regulator
relief vent

rks
Microphone Senses
Gas Hissing
Regulator Hisses
When Gas Flows
ks
Microphone Senses
Gas Hissing
Regulator Hisses
When Gas Flows

ophone Senses
Gas Hissing Filters Background Noise
How It Works
Gas Appliance in
Home is Turned On
Microp
Ga
Regulator Hisses
When Gas Flows

Detects and Identifies Gas Events
Filters Background Noise

slope

step change
slope

slope
step change

slope
step change
k-nearest neighbor
classiﬁer

Evaluated system in nine homes

496 staged gas events from 6 appliance types

321 trials used in compound

~95% accuracy

~95% accuracy
~87% accuracy, minimal calib.

mplabN G T O N
N. Patel Computer Science
& Engineering
Electrical
Engineering
design + use +
build
ElectriSense InGen
SqueezeBlock
LightW
GripSe

Supply
Load
Inductor
Switched Mode Power Supply

switching generates high frequency Electro
Magnetic Interference (EMI)
Supply
Load
Inductor
EMI
Switched Mode Power Supply

capture system

capture system
any electrical
outlet

capture system
any electrical
outlet
Power Line
Interface
(High Pass Filter)
Corner frequency: 36.7 kHz
Flat response: 50 kHz - 30 MHz
High voltage capacitors for
isolation

capture system
any electrical
outlet
Power Line
Interface
(High Pass Filter)
Digitizer
&
Frequency Transform
isolation
Sampling Frequency: 1 MHz
ADC: 12-bits
FFT width: 4096
Bin width: 244 Hz

capture system
any electrical
outlet
Power Line
Interface
(High Pass Filter)
Digitizer
&
Frequency Transform
Event Processor
&
Classiﬁer
isolation
Sampling Frequency: 1 MHz
ADC: 12-bits
FFT width: 4096
Bin width: 244 Hz

time
frequency

what EMI on the power line looks like
PC Lamp
time(s)
frequency (kHz)

features
Laptop
Unplugged
Lamp
TV
time(s)
frequency (kHz)
PC

LampPC

Diﬀerence Vector
LampPC

Diﬀerence Vector
Center: 52
Width: 2
Amplitude: 23
Center: 104
Width: 1.5
Amplitude: 16
for each peak are extracted.three
features
LampPC

event classiﬁcation
three
features

event classiﬁcation
three
features
nearest neighbor
classiﬁer
K-Nearest Neighbor
K=1
Euclidean distance metric
Inverse weighting

Evaluated system in seven homes

2576 staged electrical events

10-20 devices per home

~94% accuracy

~94% accuracy
~89% accuracy, minimal calib.

future work

U N I V E R S I T Y O F WA S H I N G T O N
& Engineering
Electrical
Engineering
design + use +
build
ElectriSense InGen
SNUPI
Static E-
CoughSense
SpiroSmart
ElderCare
Lullaby
Mobility
Rehabilitation
GasSense
HydroSense
InnerSol

toilet
78.4 gallons
shower
10.4 gallons
bath
0.0 gallons
bathroom sink 1
1.2 gallons
bathroom sink 2
0.8 gallons
shower
52.4 gallons
bath
6.5 gallons
bathroom sink 1
3.2 gallons
bathroom sink 2
2.4 gallons
today‘s usage: hot vs cold

water tower
water tower
thermal
expansion
tank
hose
spigot
utility water
meter
pressure
regulator
laundry
bathroom 1
hot
water
heater bathroom 2
dishwasher
incoming cold
water from
supply line
kitchen

70
50
30
pressure(psi)
70
50
30
pressure(psi)
hydrosense sensor stream
water tower
water tower
thermal
expansion
tank
hose
spigot
utility water
meter
pressure
regulator
laundry
bathroom 1
hot
water
heater bathroom 2
dishwasher
incoming cold
water from
supply line
kitchen

water tower
water tower
thermal
expansion
tank
hose
spigot
utility water
meter
pressure
regulator
laundry
bathroom 1
hot
water
heater bathroom 2
dishwasher
incoming cold
water from
supply line
kitchen

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).
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, Citeseer (2009), 235–244.

10 psi

10 psi
7.32 psi

10 psi
7.32 psi
15 Hz

10 psi
7.32 psi
15 Hz
200 ms

10 psi
7.32 psi
15 Hz
200 ms
kitchen sink

10 psi
7.32 psi
15 Hz
200 ms
kitchen sink
upstairs toilet
downstairs toilet

10 psi
7.32 psi
15 Hz
200 ms
kitchen sink kitchen sink hot
upstairs toilet
downstairs toilet
cold

10 psi
7.32 psi
features: single instance
15 Hz
200 ms

70
50
30
pressure(psi)
70
50
30
pressure(psi)
features: single instance

70
50
30
pressure(psi)
70
50
30
pressure(psi)

70
50
30
pressure(psi)
70
50
30
pressure(psi)
features: sequence

70
50
30
pressure(psi)
70
50
30
pressure(psi)
10 sec
features: sequence

70
50
30
pressure(psi)
70
50
30
pressure(psi)
10 sec compound
features: sequence

70
50
30
pressure(psi)
70
50
30
pressure(psi)
10 sec compound
features: sequence
time of day

y1 y2 y3 y4 y5 y6
x1 x2 x3 x4 x5 x6
CRF: conditional random ﬁeld
sequence model

classiﬁcation
70
50
30
pressure(psi)
70
50
30
pressure(psi)

kitchen sink kitchen sink
toilet
bathroom sink
classiﬁcation
70
50
30
pressure(psi)
70
50
30
pressure(psi)

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.

accuracy

ﬁxture category level
e.g., a faucet
accuracy

e.g., a faucet
accuracy
~95%

ﬁxture level
e.g., upstairs bathroom faucet
e.g., a faucet
accuracy
~95%

ﬁxture level
e.g., a faucet
accuracy
~95%
~92%

ﬁxture level
e.g., a faucet
valve level
e.g., upstairs bathroom faucet, hot valve
accuracy
~95%
~92%

ﬁxture level
e.g., a faucet
valve level
e.g., upstairs bathroom faucet, hot valve
accuracy
~95%
~92%
~80%

valvelevelaccuracy(%)
training amount (days)
~80%

2 4 6 8 10
55
60
65
70
75
80
10 fold cross validation, CRF
~80%

2 4 6 8 10
55
60
65
70
75
80
10 fold cross validation, CRF
300 events
500 events
700 events
900 events
~80%

need a semi-supervised approach

sor Shwetak N. Patel Computer Science
& Engineering
Electrical
Engineering
design + use +
build
ElectriSense InGen
SNUPI
ense
mart
GasSense
uToSNUPI
GasSense
HydroSense
ElderCare
Mobility
Rehabilitation
HydroSense
future work

cycles

cycles
heated dry

cycles
heated dry
washing dishes

~7:30AM

ﬁlling coffee pot
coffee pot running
~7:30AM

bathroom usage

bathroom usage
leaky ﬂapper valve

ElectriSense InGen
SNUPI
ense
mart
re
GasSense
HydroSense
uToSNUPI
Static E-Field
GasSense
HydroSense
ElderCare
Lullaby
Mobility
Rehabilitation
HydroSense
InnerSol

ElectriSense InGen
SNUPI
ense
mart
re
GasSense
HydroSense
uToSNUPI
Static E-Field
GasSense
HydroSense
ElderCare
Lullaby
Mobility
Rehabilitation
HydroSense
InnerSol
low-cost, disaggregated sensing

ElectriSense InGen
SNUPI
ense
mart
re
GasSense
HydroSense
uToSNUPI
Static E-Field
GasSense
HydroSense
ElderCare
Lullaby
Mobility
Rehabilitation
HydroSense
InnerSol
working towards semi-supervised training

ElectriSense InGen
SNUPI
ense
mart
re
GasSense
HydroSense
uToSNUPI
Static E-Field
GasSense
HydroSense
ElderCare
Lullaby
Mobility
Rehabilitation
HydroSense
InnerSol
working towards semi-supervised training
possible to sense usage activity

Sensing for Sustainability
disaggregated sensing of water, electricity, and natural gas
University of Washington
Eric Larson
PhD Candidate in School of Electrical and Computer Engineering
UbiComp Lab
electrical
engineering
computer
science
eclarson.com
eclarson@uw.edu
@ec_larson
presented at
December 7th, 2012
ubicomplabU N I V E R S I T Y O F WA S H I N G T O N
& Engineering
Electrical
Engineering
design + use +
build
ElectriSense InGen
SNUPI
Static E-Fiel
CoughSense
SpiroSmart
ElderCare
Mobility
Rehabilitation
GasSense
HydroSense
ElectriSense InGen
uTouch
WalkTy
SNUPI
Static E-Field
CoughSense
SpiroSmart
ElderCare
Lullaby
Mobility
Rehabilitation
GasSense
HydroSense
InnerSol
CoughSense
ElderCare
Lullaby
Mobility
Rehabilitation
GasSense
HydroSense
InnerSol
Gabe Cohn Sidhant Gupta Jon Froehlich

Sensing for Sustainability: Disaggregated Sensing of Electricity, Gas, and Water

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