SpotLight : Focusing on Energy Consumption of Individuals

1. SpotLight : Focusing on Energy Consumption of Individuals Younghun Kim Zainul M. Charbiwala Akhilesh Singhania Mani B. Srivastava

2. What is SpotLight? • An WSN application • Profile individual’s energy consumption • Provide real-time energy profile • Help users identify areas of inefficient power consumption • A prototype implementation • May not be a thorough solution • May have many limitations • But can point out interesting points and observations

3. Overview • Motivation • Problem description and discussion • Related work • Prototype system implementation and design • Evaluations • Conclusion • Future work

4. Motivation • How much energy do I consume? • What about in the lab and home? • I am unaware of my energy usage pattern • Hard for me to optimize energy consumption • What about other resources? • Water and Gas

5. Levels of Electricity Monitoring

6. Fundamental Questions • How to measure energy consumption of an appliance • Direct Measurement • Easy and precise but may not be scalable • Indirect Measurement • Hard and less precise but may be scalable • How to know who is using it • Annotation : Users write down when and which • Inference • From absolute position • From relative position among users and appliances • And on/off status of appliances

7. Intuitive Ways to Answer to the Questions • How do we know someone is using appliances • An appliance is on • A is in front of the appliance and interacts with it • How dowe say someone is wasting energy • A turned on a light • A left it on and went somewhere • Some ambiguities from socio-economical reasons • On behalf of B, A is using an appliance. Who is responsible for the energy consumption? • A cooks for others, who is responsible?

8. Objectives • Give some useful information for users • Amount of wasted energy • Amount of useful energy per each appliance or each person • Construct fair and comprehensive policy • Less arguable policy • Agreeable policy • Reliable policy • Develop an easy deployable and usable system • Less human intervention • Off-the-shelf experience

9. Related Works • Intelligent Powermeter, Der intelligente Stromzähler, Germany • Real-time Powermeter monitoring device From http://www.enbw.com

10. Related Works • Demand response system, Berkeley • Control HVAC/Lighting • Based upon occupancy or demand From Demand Response Enabling Technology Development Tech report, Berkeley

11. Related Works • MEMS nonintrusive electrical monitoring device, Leland et. al., Berkeley • Electrical monitoring • Energy scavenging for motes From Energy scavenging power sources for household electrical monitoring, Eli S. Leland et al., Berkeley

12. Related Works • At the Flick of a Switch, Shwetak N. Patel et al., Gatech • Detecting and Classifying Unique Electrical Events on the Residential Power Line • Less intrusive, real-time From At the Flick of a Switch, Shwetak N. Patel et. al, Gatech

13. Related Works • Ambient Devices • Provide users with customized information From http://www.ambientdevices.com

14. Classes of Appliances • Underlying Assumptions • Someone, using appliances, is benefiting from them • Indirect use, ambiguous use: not a technical question or even harder to be addressed • Classification of appliances • Class I: Serviced inside physical vicinity. E.g. Television, coffee machine and most of home appliances. Focus of our work • Class II: Serviced with remote access. E.g. servers, networked printers and some office appliances • Class III: Serviced independent of access. E.g. Refrigerator, alarm clocks

15. Definition : Service Range • Service range of an appliance is a user defined vicinity boundary • Token : Abstraction that indicates current status of usage

16. Token Issue/Expire : Case I

17. Token Issue/Expire : Case II

18. Prototype System Implementation • Users carry tag motes • Appliances instrumented with a power meter and tagreader • Server to store and process data

19. Hardware Prototype • MicaZ ‘appliance’ motes • MicaZ+COTS Power Measurement device • Measuring power consumption and RSSIvalue • MicaZ ‘tag’ motes • MicaZ+Accelerometer • Triggered by accelerometer and broadcast its ID • MicaZ ‘base’ mote • MicaZ+Laptop • Processing and backup • Sensorbase • Remote spatio-temporal DB

20. Service Range and Radio Signal Strength • MicaZ Mote as Tags and Readers • RSSI values give rough range information • RSSIbased localization technique makes use of it • RSSIgives rough range information between a tag and reader • Relative position could be inferred • Limitation : Service Range cannot be arbitrary • Note : Home and office setting is static

21. Technical Challenges • RSSI values vary depending upon Antenna characteristics, users movement, obstruction of LOS and others • Simple threshold may not be good • In some cases, • Calibration helps : Antenna Characteristics • Hysteresis helps : Noise along boundary • Those hamper usability issues

22. Evaluation Setting : Ideal Service Range • 4 ‘appliance’ Motes • Three SOS S/W modules : PM, tag reader, UART • 2 ‘tag’ Motes • One SOS S/W module : tag • 1 BaseNode Mote • One SOS module : Snooping

23. Measured Service Range : Coffee Maker

24. Measured Service Range : Bedroom Lamp

25. Measured Service Range : Livingroom Lamp

26. Measured Service Range : TV

27. Some Observations • Service Range for RFID tag I • about -26 of the living-room lamp • about -20 of the TV • about -5 of the coffee machine • Service Range for RFID tag II • about -22 of the living-room lamp • about -14 of the TV • about -8 of the coffee machine => Need to calibrate tag characteristics => Need to reject noise effect

28. Three Schemes • Unified Token Scheme • Service range = Single RSSI value • Calibrated Token Scheme • Service range = Tag specific RSSIvalue • Hysteresis Token Scheme • Service range = Tag specific RSSI value • Expire tokens when RSSIovercomes Hysteresis

29. Ground Truth • Using Campaignr with a local Sensorbase • Took Pictures and annotated users’ status • Sampling rate : 10s • Ground Truth tells us • Who is/are using appliances • Who has/have left appliances on/off

30. Evaluation in terms of False Positive and Negative : TV

31. Evaluation in terms of False Positive and Negative : Bedroom Lamp

32. Evaluation in terms of False Positive and Negative : Livingroom Lamp

33. Performance of Various RSSI, Vicinity, Token Mechanisms

34. Appliance Power and Energy Consumption per User

35. Useful/Wasted Power and Energy for TV

36. Useful and Wasted Consumption for all Appliances

37. Conclusion • Proof-of-concept implementation • Presented a system that profiles energy consumption pattern in individual level • Detection scheme was reasonably successful under simple evaluation setting • Presented various information that encourages people optimize their energy consumption pattern • But the system needs to • Be easy to deploy and use • Have comprehensive and fair policy

38. Future work • More comprehensive view needed • Deployment under more complex setting needed • Monitor other resources : Water • Cope with various RSSI uncertainties or try other localization technique? • Improve usability and scalability • Cope with various limitations