The document presents an approach for energy disaggregation called Electrisense that uses signatures in the power line to detect individual electronic appliances in homes. It discusses different approaches like distributed, single point, and infrastructure mediated sensing and their limitations. The presented approach uses a single point sensor and classifies appliances based on their characteristic frequency signatures coupled into the power line from their switching mode power supplies. An experiment across 7 homes accurately detected over 2500 appliance events with 92% accuracy within homes and 96% accuracy across homes for some appliances, showing the potential of this noise-based sensing and signature portability. However, it only works on electronics and in controlled settings, and the approach may break with new appliances or interference.

1. Electrisense Presented by: Nipun Batra

2. Buildings contribute ~40% to total energy

3. "If you can't measure it, you can't reduce it"

4. Current electricity bill

5. But groceries got it right!

6. Appliance level feedback can reduce energy by 15%

7. Can electrical usage tell more? Applications in healthcare

8. Energy disaggregation 1. Distributed sensing 2. Single point sensing 3. Infrastructure mediated sensing

9. Distributed sensing Maintenance and deployment cost

10. Single point sensing Doesn't do well for electronics

11. Infrastructure mediated 1. Transients when mechanical switches toggled 2. Doesn't generalize 3. Expensive

12. "Single point sensing automatic detection of electronic devices in a home"

13. Piece of pie Only electronic appliances :(

14. Approach • Electronics and some lighting run on SMPS • Each SMPS has characteristic frequency • These frequencies coupled into power line

15. Classification done using K Nearest neighbour

16. Experimental setup 1. Dependent variable: Event detection 2. Independent variable: Time, Home, Appliance, #similar appliances 3. Baseline: None 4. Ground truth: Manual annotation 5. Bounds: None meaningful

17. Experiment 1. 7 homes (7-20 appliances) 2. 6 homes- 1 day data, 7th home- 6 months 3. Events labeled using portable computer 4. 2576 events in all

18. Within home experiment 1. 10 fold KNN on each home 2. Accuracy: 92% 3. Spatially close CFLs got confused

19. Across homes- Signature portability 1. EMI signature maintained across homes :) 2. Tested for 4 appliances 3. 10 fold CV, 96% accuracy

20. Across homes- Signal consistency 1. 10 Dell monitors used 2. Train on Hi and test on Hj 3. 100% accuracy

21. Why this paper rocks 1. Electronics can be detected 2. Noise can be signal 3. Electronics becoming increasingly common 4. Signal portability

22. (Potential) Fallacies 1. Appliances reduce EMI emission 2. No baseline :( 3. Only done in controlled setting- appliance fail, new appliances 4. Only looking for specific signatures- KNN can break!!