Strategies for Large Scale Deployment of Energy Monitoring and Sensing in the Home

Transcription

1 Strategies for Large Scale Deployment of Energy Monitoring and Sensing in the Home Shwetak N. Patel University of Washington

2 Sensing in the home Focus of this talk Resource monitoring Low-power wireless sensing Shwetak N. Patel - University of Washington 2

3 Living Laboratories Shwetak N. Patel - University of Washington 3

4 Non-expert deployment of energy monitoring technology Disaggregated energy use 4

5

6 Feedback Interfaces

7 Value for the stakeholders Conservation and actionable feedback New model for demand response Validation and verification of conservation activities No truck roll or costly rollouts No need to go into the home

8 coffee bean maker grinder refrigerator microwave Distributed Direct Sensing

9 Infrastructure Mediated Sensing Use the utility infrastructure as the sensor Ubicomp 2007, Pervasive 2008

10 PowerLine Event Detection Single sensor on the power line can detect the use of most electrical devices Ubicomp 2007, 2010, Pervasive

11 Existing Approaches Past work has looked at the current/voltage relationship (NILM, Hart et al).

12 Our Approach: Your noise is my signal Instead of just current, sample the voltage at a high rate Only have to sample one location Look at electrical noise on the power lines Electronic devices produce noise

13 Switch Mode Power Supplies (SMPS) Small, efficient Very popular Produce continuous noise 13

14 For example 14

15 Whole-House Current Consumption Not end-user installable Requires an electrician 15

16 Contact-Less Whole-House Current Consumption Sensor Detects the magnetic field behind the metal Within 4% of true power Also provides additional features for classification CHI

17 Detectable Events Microwave door Microwave Coffee maker Electric stove Refrigerator door Refrigerator Electric oven Electric oven door Dishwasher Garbage disposal Bathroom exhaust fan Incandescent lights via a wall switch Fluorescent lights via a wall switch Lights via a dimmer wall switch Garage door opener Dryer HVAC/Heat Pump Ceiling fan Laptop power adapter Television

18 Water Sensing Single-point sensor of water usage Identifies water usage activity down to a fixture level (e.g., toilet) Provides estimates of flow at each fixture hydrosense Ubicomp 2009, Pervasive 2011, PMC Journal 2011

19 traditional inline water meter

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

21 Fixture-level Event Detection 21

22 Gas Sensing

23 GasSense

24 New approaches for low-power sensing 24

25 hard to maintain

26 hard to maintain 26

27 S N U P I ensor odes tilizing owerline nfrastructure Ubicomp

28 Powerline Coupling Receiver plugged into powerline Powerline acts as receiving antenna Transmitter sends data to nearest powerline 28

29 Traditional Over-the-Air Wireless 29

30 Powerline Coupling strongly dependent on frequency ISM band 27 MHz 30

31 Hardware Overview fully-programmable 3.8 cm wireless sensing low throughput (10kps) whole-home long battery life 65 uw TX 3.8 cm 1.4 cm 17 grams including battery 31

32 mw 60 SNUPI Peak Power Consumption Communication (Radio) Computation (CPU) Total Power Existing Nodes SNUPI 32

33 100 mw SNUPI Peak Power Consumption Communication (Radio) Computation (CPU) Total Power 10 mw 1 mw 100 µw 10 µw Existing Nodes SNUPI 33

34 100 mw SNUPI Peak Power Consumption Communication (Radio) Computation (CPU) Total Power 10 mw 1 mw 100 µw 10 µw Existing Nodes SNUPI Communication/Computation Power Relationship Reversed 34

35 100 mw SNUPI Peak Power Consumption Communication (Radio) Computation (CPU) Total Power 10 mw 1 mw 100 µw 10 µw Existing Nodes SNUPI 2 Orders of Magnitude Reduction in Communication Power 35

36 100 mw SNUPI Peak Power Consumption Communication (Radio) Computation (CPU) Total Power 10 mw 1 mw 100 µw 10 µw Existing Nodes SNUPI 1 Order of Magnitude Reduction in Total Peak Power 36

37 Uses Long-lived sensor deployments Hard to reach areas 37

38 Making it Available - Scale 38

39 Questions? Special Thanks and Acknowledgements: Students: Jon Froehlich, Sidhant Gupta, Eric Larson, Gabe Cohn, Tim Campbell Faculty: James Fogarty, James Landay, Les Atlas, Brian Otis, Matt Reynolds Shwetak N. Patel - University of Washington 39