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Consumer Centered Calibration End Use Water Monitoring

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Presented at WikiEnergy / NILM Workshop 2014

Presented at WikiEnergy / NILM Workshop 2014

Published in: Engineering, Business, Technology

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  • 1. consumer centered calibration in end-use water monitoring eric c. larson | eclarson.com Assistant Professor Computer Science and Engineering
  • 2. on average, a consumer can save 15-25%
  • 3. lake mead 1983
  • 4. lake mead 2011
  • 5. we are using water faster than it is being replenished Pacific Institute for Studies in Development, Environment, and Security, 2011
  • 6. we are using water faster than it is being replenished Pacific Institute for Studies in Development, Environment, and Security, 2011
  • 7. $2,994.83
  • 8. more awareness
  • 9. Fabricated"Unit 16 Water"Meter"Design 15 Turbine Insert Thermistor Flow image: LBNL
  • 10. Fabricated"Unit 16 Water"Meter"Design 15 Turbine Insert Thermistor Flow image: LBNL
  • 11. Fabricated"Unit 16 Water"Meter"Design 15 Turbine Insert Thermistor Flow image: LBNL
  • 12. meters flow rate fixture flow inline water Fabricated"Unit 16
  • 13. meters flow rate fixture flow inline water water pressure pressure sensor Fabricated"Unit 16
  • 14. meters flow rate fixture flow inline water water pressure pressure sensor Fabricated"Unit 16
  • 15. meters flow rate fixture flow inline water water pressure pressure sensor machine learning estimated Fabricated"Unit 16
  • 16. HydroSense
  • 17. • central sensing point • easy to install • low cost • can observe every fixture HydroSense
  • 18. • central sensing point • easy to install • low cost • can observe every fixture HydroSense BelkinEcho
  • 19. • 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
  • 20. • 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
  • 21. • 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
  • 22. kitchen sink upstairs toilet downstairs toilet
  • 23. kitchen sink upstairs toilet downstairs toilet days 33 33 30 27 33 events 2374 3075 4754 2499 2578 events/day 71.9 93.2 158.5 92.6 78.1 = days 33 33 30 events 2374 3075 4754 events/day 71.9 93.2 158.5
  • 24. kitchen sink upstairs toilet template matching unknown event downstairs toilet
  • 25. kitchen sink upstairs toilet template matching unknown event downstairs toilet
  • 26. 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).
  • 27. 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).
  • 28. 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).
  • 29. 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).
  • 30. 70 50 30 pressure(psi) 70 50 30 pressure(psi) initial study: staged events
  • 31. 70 50 30 pressure(psi) 70 50 30 pressure(psi) initial study: staged events kitchen sink kitchen sink
  • 32. 70 50 30 pressure(psi) 70 50 30 pressure(psi) natural water use
  • 33. how well does single point sensing work in a natural setting?
  • 34. longitudinal evaluation
  • 35. longitudinal evaluation
  • 36. 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.
  • 37. 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
  • 38. natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi)
  • 39. natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) 8AM 2 minutes
  • 40. bathroom sink natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) 8AM 2 minutes
  • 41. toilet bathroom sink natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) 8AM 2 minutes
  • 42. kitchen sink kitchen sink toilet bathroom sink natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) 8AM 2 minutes
  • 43. kitchen sink kitchen sink toilet bathroom sink natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) template matching: 98% 74% 8AM 2 minutes
  • 44. kitchen sink kitchen sink toilet bathroom sink natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) template matching: 98% 74% 10 fold cross validation 8AM 2 minutes
  • 45. kitchen sink kitchen sink toilet bathroom sink natural water usage 70 50 30 pressure(psi) 70 50 30 pressure(psi) template matching: 98% 74% 10 fold cross validation 35% 8AM 2 minutes minimal
  • 46. two step process: segmentation 70 50 30 pressure(psi) 70 50 30 pressure(psi) standard deviation smoothing operation pressure deltas local maxima (for adjustments/compound)
  • 47. 70 50 30 pressure(psi) 70 50 30 pressure(psi) pressure drop min pressure duration min local maxima max derivative max amplitude LPC frequency characteristics sink flow meter two step process: features extraction xs 1
  • 48. 70 50 30 pressure(psi) 70 50 30 pressure(psi) pressure drop min pressure duration min local maxima max derivative max amplitude LPC frequency characteristics sink flow meter two step process: features extraction xs 1 xd 2 xd 3 xd 4 xd 5 xd 6 xd 7
  • 49. machine learning overview labeled RF classifier predicted fixture
  • 50. machine learning overview minimal set of labels for calibrationlabeled RF classifier predicted fixture
  • 51. machine learning overview minimal set of labels for calibrationlabeled RF classifier predicted fixture one or two examples per fixture
  • 52. machine learning overview minimal set of labels for calibrationlabeled RF classifier predicted fixture one or two examples per fixture accuracy 40-60%
  • 53. 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!
  • 54. 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?
  • 55. active learning labeled classifier 1 classifier N… randomforest
  • 56. active learning labeled classifier 1 classifier N unlabeled … randomforest
  • 57. active learning labeled classifier 1 classifier N unlabeled disagree? … randomforest
  • 58. active learning labeled classifier 1 classifier N unlabeled disagree? max margin of committee … randomforest need a label margin < 20%
  • 59. active learning leveraging the homeowner
  • 60. active learning leveraging the homeowner • select low confidence margin examples
  • 61. active learning leveraging the homeowner • select low confidence margin examples • ask homeowner for label
  • 62. 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
  • 63. 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.
  • 64. 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.
  • 65. 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.
  • 66. 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.
  • 67. 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.
  • 68. 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
  • 69. 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
  • 70. 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
  • 71. 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
  • 72. totals totals 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 accuracy
  • 73. totals totals 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 accuracy
  • 74. totals totals 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 accuracy
  • 75. totals totals 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 accuracy
  • 76. usion is the secondary bathroom shower for the master bathroom shower. -374 8.5 1.5 -3 6.1 72 1.1 0.8 5.0 0.7 1.5 0.6 -7 18 92 0.6 5.4 2.6 4.9 3.4 2.3 3.5 0.9 5.1 -7 18 0.9 92 1.4 4.9 0.7 4.4 1.4 1.4 7.3 1.0 0.7 3.2 -753 2.1 -1 4.2 67 0.6 2.4 0.8 31 2.5 5.2 -5 1.2 5.0 0.6 18 4.2 88 1.8 7.5 1.5 1.6 5.7 1.0 -5 1.2 0.5 5.3 4.6 10 1.8 89 1.7 1.7 1.4 0.7 3.2 -2 2.9 0.6 81 12 -2 0.8 94 0.6 8.1 -1 6.7 68 -2 2.8 42 -1 1.9 2.9 0.5 25 0.8 95 0.7 -1 2.2 3.1 10 1.3 96 6.6 -290 -3 1.1 0.7 59 18 -3 0.9 0.9 15 60 er close,1 open,2 k close,3 open,4 er close,5 open,6 k close,7 open,8 et close,9 open,10 r close,11 open,12 k close,13 open,14 t open,15 e close,16 open,17
  • 77. usion is the secondary bathroom shower for the master bathroom shower. -374 8.5 1.5 -3 6.1 72 1.1 0.8 5.0 0.7 1.5 0.6 -7 18 92 0.6 5.4 2.6 4.9 3.4 2.3 3.5 0.9 5.1 -7 18 0.9 92 1.4 4.9 0.7 4.4 1.4 1.4 7.3 1.0 0.7 3.2 -753 2.1 -1 4.2 67 0.6 2.4 0.8 31 2.5 5.2 -5 1.2 5.0 0.6 18 4.2 88 1.8 7.5 1.5 1.6 5.7 1.0 -5 1.2 0.5 5.3 4.6 10 1.8 89 1.7 1.7 1.4 0.7 3.2 -2 2.9 0.6 81 12 -2 0.8 94 0.6 8.1 -1 6.7 68 -2 2.8 42 -1 1.9 2.9 0.5 25 0.8 95 0.7 -1 2.2 3.1 10 1.3 96 6.6 -290 -3 1.1 0.7 59 18 -3 0.9 0.9 15 60 er close,1 open,2 k close,3 open,4 er close,5 open,6 k close,7 open,8 et close,9 open,10 r close,11 open,12 k close,13 open,14 t open,15 e close,16 open,17 dishwasher laundry dishwasher laundry
  • 78. usion is the secondary bathroom shower for the master bathroom shower. -374 8.5 1.5 -3 6.1 72 1.1 0.8 5.0 0.7 1.5 0.6 -7 18 92 0.6 5.4 2.6 4.9 3.4 2.3 3.5 0.9 5.1 -7 18 0.9 92 1.4 4.9 0.7 4.4 1.4 1.4 7.3 1.0 0.7 3.2 -753 2.1 -1 4.2 67 0.6 2.4 0.8 31 2.5 5.2 -5 1.2 5.0 0.6 18 4.2 88 1.8 7.5 1.5 1.6 5.7 1.0 -5 1.2 0.5 5.3 4.6 10 1.8 89 1.7 1.7 1.4 0.7 3.2 -2 2.9 0.6 81 12 -2 0.8 94 0.6 8.1 -1 6.7 68 -2 2.8 42 -1 1.9 2.9 0.5 25 0.8 95 0.7 -1 2.2 3.1 10 1.3 96 6.6 -290 -3 1.1 0.7 59 18 -3 0.9 0.9 15 60 er close,1 open,2 k close,3 open,4 er close,5 open,6 k close,7 open,8 et close,9 open,10 r close,11 open,12 k close,13 open,14 t open,15 e close,16 open,17 dishwasher laundry dishwasher laundry
  • 79. implications for NILM
  • 80. 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.
  • 81. 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?
  • 82. 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?
  • 83. 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?
  • 84. 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
  • 85. limitations
  • 86. limitations responsive cloud architecture
  • 87. limitations responsive cloud architecture multiple people in a home
  • 88. 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.
  • 89. 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