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buildsys15

Nipun Batra
November 03, 2015

 buildsys15

If you can measure it, can you improve it? Exploring the value of energy disaggregation

Nipun Batra

November 03, 2015
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  1. Nipun Batra, Amarjeet Singh, Kamin Whitehouse Buildsys 2015 If You

    Measure It, Can You Improve It? Exploring The Value of Energy Disaggregation through actionable feedback 1
  2. General eco feedback vs Actionable Feedback Eco feedback Misc. 22%

    Light 10% Fridge 11% HVAC 56% Power (W) 0 175 350 525 700 Power (W) 0 175 350 525 700 Home 1 Home 2 Actionable feedback Fridge consumption over 24 hours Misc. 22% Light 10% Fridge 11% HVAC 56%
  3. General eco feedback vs Actionable Feedback Eco feedback Misc. 22%

    Light 10% Fridge 11% HVAC 56% Power (W) 0 175 350 525 700 Power (W) 0 175 350 525 700 Home 1 Home 2 Actionable feedback Fridge consumption over 24 hours Misc. 22% Light 10% Fridge 11% HVAC 56% High power state
  4. General eco feedback vs Actionable Feedback Eco feedback Misc. 22%

    Light 10% Fridge 11% HVAC 56% Power (W) 0 175 350 525 700 Power (W) 0 175 350 525 700 Home 1 Home 2 Actionable feedback Fridge consumption over 24 hours Misc. 22% Light 10% Fridge 11% HVAC 56% High power state High power state
  5. General eco feedback vs Actionable Feedback Eco feedback Misc. 22%

    Light 10% Fridge 11% HVAC 56% Power (W) 0 175 350 525 700 Home 2 Actionable feedback Fridge consumption over 24 hours Your fridge defrosts too much, wasting 30% energy Misc. 22% Light 10% Fridge 11% HVAC 56%
  6. Approach overview- How to give feedback Power (W) 0 175

    350 525 700 Specific features of trace to infer why energy usage is high Length of duty cycles
  7. Approach overview- How to give feedback Power (W) 0 175

    350 525 700 Specific features of trace to infer why energy usage is high Actual power value
  8. Feedback methods on Fridge and HVAC Both appliances commonly found

    across homes Others 38% Fridge 8% HVAC 54%
  9. Can we give such feedback? Disaggregated traces Power (W) 0

    350 700 NILM Household aggregate Submetered traces Power (W) 0 350 700 Submeter sensor 0 2000 4000 0 2000 4000 Smart meter
  10. Do disaggregated traces provide features needed for providing feedback? Disaggregated

    traces Power (W) 0 350 700 NILM Household aggregate Submetered traces Power (W) 0 350 700 Submeter sensor 0 2000 4000 0 2000 4000 Smart meter
  11. 0 125 250 375 500 Fridge is a duty cycle

    based appliance; compressor turns ON and OFF periodically
  12. Night hours typically have “baseline” usage 0 175 350 525

    700 Baseline duty % = Median duty % in the night
  13. Defrost energy 0 175 350 525 700 Defrost energy =

    Energy consumed in defrost state + Extra energy consumed in next few compressor cycles
  14. Defrost energy 0 175 350 525 700 Defrost energy =

    Energy consumed in defrost state + Extra energy consumed in next few compressor cycles
  15. Usage energy 0 175 350 525 700 Usage energy =

    Extra energy consumed over baseline
  16. 13 out of 95 homes can be given feedback based

    on usage energy saving upto 23% fridge energy
  17. 13 out of 95 homes can be given feedback based

    on usage energy saving upto 23% fridge energy NILM algorithms show poor accuracy in identifying homes which can be given feedback based on usage energy
  18. 17 out of 95 homes can be given feedback on

    excess defrost saving upto 25% fridge energy
  19. Such feedback can’t be given with disaggregated traces, since these

    techniques fare poorly on defrost detection.
  20. Benchmark NILM algorithms on our data set give accuracy comparable

    or better than state-of-the-art Kolter 2012 Parson 2012 Parson 2014 Batra 2014 CO FHMM Hart Error Energy % 0 17.5 35 52.5 70
  21. 0 125 250 375 500 “Average” error in energy would

    be low even if NILM predicted this
  22. 0 1000 2000 3000 4000 People typically turn up the

    temperatures when they leave home
  23. Recommended Min. Temp (F) 77 79 81 83 85 2

    4 6 8 10 12 14 16 18 20 22 24 EnergyStar.gov recommended HVAC setpoint schedule Sleep Morning Work Evening
  24. Recommended Min. Temp (F) 77 79 81 83 85 2

    4 6 8 10 12 14 16 18 20 22 24 Setpoint schedule score Sleep Morning Work Evening
  25. Recommended Min. Temp (F) 77 79 81 83 85 2

    4 6 8 10 12 14 16 18 20 22 24 Setpoint schedule score Sleep Sleep score = 1 if sleep temp. > 82, (82-temp.)/4 if 78<sleep temp. <82 0 otherwise
  26. Learning HVAC setpoint 0 1000 2000 3000 4000 77 85

    5 1015 20 HVAC trace Weather Learnt setpoint
  27. Giving feedback 77 85 5 1015 20 Features from HVAC

    trace 69 75 5 10 15 20 0 1000 2000 3000 4000 77 85 5 10 15 20 Learnt setpoint Don’t need feedback Need feedback
  28. Benchmark NILM algorithms on our data set give accuracy comparable

    or better than state-of-the-art Batra 2014 CO FHMM Hart Error Energy % 0 7.5 15 22.5 30
  29. Error in prediction of minutes of HVAC usage (%) 0

    6 12 18 24 Hart FHMM CO Night Morning Morning hours which have lesser NILM accuracy are important for HVAC feedback
  30. Conclusions Appliance level data does enable actionable energy saving feedback

    BUT Results show that we need to revisit the metrics by which we measures progress