skip to main content
research-article

Monitoring massive appliances by a minimal number of smart meters

Published:27 January 2014Publication History
Skip Abstract Section

Abstract

This article presents a framework for deploying a minimal number of smart meters to accurately track the ON/OFF states of a massive number of electrical appliances which exploits the sparseness feature of simultaneous ON/OFF switching events of the massive appliances. A theoretical bound on the least number of required smart meters is studied by an entropy-based approach, which qualifies the impact of meter deployment strategies to the state tracking accuracy. It motivates a meter deployment optimization algorithm (MDOP) to minimize the number of meters while satisfying given requirements to state tracking accuracy. To accurately decode the real-time ON/OFF states of appliances by the readings of meters, a fast state decoding (FSD) algorithm based on the hidden Markov model (HMM) is presented to track the state sequence of each appliance for better accuracy. Although traditional HMM needs O(t22N) time complexity to conduct online sequence decoding, FSD improves the complexity to O(tnU+1), where n < N and U is an upper bound of the simultaneous switching events. Both MDOP and FSD are verified extensively using simulations and real PowerNet data. The results show that the meter deployment cost can be saved by more than 80% while still getting over 90% state tracking accuracy.

References

  1. S. Dawson-Haggerty, S. Lanzisera, J. Taneja, R. Brown, and D. Culler. 2012. &commat;scale: Insights from a large, long-lived appliance energy WSN. In Proceedings of the ACM IPSN. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Energy hub. 2009. Energy hub. http://www.energyhub.net/.Google ScholarGoogle Scholar
  3. L. Farinaccio and R. Zmeureanu. 1999. Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy Build. 30, 3, 245--259.Google ScholarGoogle ScholarCross RefCross Ref
  4. G. D. Forney, Jr. 1973. The Viterbi algorithm. Proc. IEEE 61, 3, 268--278.Google ScholarGoogle ScholarCross RefCross Ref
  5. GreenBox. 2009. Green box. http://www.getgreenbox.com/.Google ScholarGoogle Scholar
  6. S. Gupta, M. S. Reynolds, and S. N. Patel. 2010. Electrisense: Single-point sensing using EMI for electrical event detection and classification in the home. In Proceedings of Ubicomp'10. ACM, New York, NY, 139--148. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. X. Hao, Y. Wang, C. Wu, L. Song, and Y. Wang. 2012a. Smart meter deployment for efficient appliance state monitoring. In Proceedings of the 3rd IEEE International Conference on Smart Grid Communications. 25--30.Google ScholarGoogle Scholar
  8. X. Hao, Y. Wang, C. Wu, A. Y. Wang, and L. Song. 2012b. Proof of approximation ratio and complexity of MDOP algorithm. http://wcy.name/papers/proof2.pdf.Google ScholarGoogle Scholar
  9. G. W. Hart. December 1992. Nonintrusive appliance load monitoring. Proc. IEEE 80, 12, 1870--1891.Google ScholarGoogle Scholar
  10. X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler. 2009a. Design and implementation of a high-fidelity AC metering network. In Proceedings of ACM IPSN'09. IEEE Computer Society, 253--264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. X. Jiang, M. Van Ly, J. Taneja, P. Dutta, and D. Culler. 2009b. Experiences with a high-fidelity wireless building energy auditing network. In Proceedings of SenSys'09. ACM, New York, NY, 113--126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. D. Jung and A. Savvides. 2010. Estimating building consumption breakdowns using on/off state sensing and incremental sub-meter deployment. In Proceedings of SenSys'10. ACM, New York, NY, 225--238. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. M. A. Kazandjieva, B. Heller, P. Levis, and C. Kozyrakis. 2009. Energy dumpster diving. In Proceedings of the 2nd Workshop on Power Aware Computing (HotPower'09).Google ScholarGoogle Scholar
  14. Y. Kim, T. Schmid, Z. M. Charbiwala, and M. B. Srivastava. 2009. ViridiScope: Design and implementation of a fine grained power monitoring system for homes. In Proceedings of Ubicomp'09. ACM, New York, NY, 245--254. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. S. Leeb, S. Shaw, and J. L. Kirtley, Jr. 1995. Transient event detection in spectral envelope estimates for nonintrusive load monitoring. IEEE Trans. Power Delivery 10, 3, 1200--1210.Google ScholarGoogle ScholarCross RefCross Ref
  16. J. Lifton, M. Feldmeier, Y. Ono, C. Lewis, and J. A. Paradiso. 2007. A platform for ubiquitous sensor deployment in occupational and domestic environments. In Proceedings of IPSN'07. ACM, New York, NY, 119--127. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. L. K. Norford and S. B. Leeb. 1996. Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy Build. 24, 1, 51--64.Google ScholarGoogle ScholarCross RefCross Ref
  18. Oxford St. Hughs College Data. 2010. Energy and water conservation. http://www.st-hughs.ox.ac.uk/welfare-and-facilities/estates/energy-and-water-conservation.Google ScholarGoogle Scholar
  19. S. N. Patel, T. Robertson, J. A. Kientz, M. S. Reynolds, and G. D. Abowd. 2007. At the flick of a switch: Detecting and classifying unique electrical events on the residential power line. In Proceedings of Ubicomp'07. 271--288. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. A. Rowe, M. Berges, and R. Rajkumar. 2010. Contactless sensing of appliance state transitions through variations in electromagnetic fields. In Proceedings of BuildSys'10. ACM, New York, NY, 19--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Z. C. Taysi, M. A. Guvensan, and T. Melodia. 2010. Tinyears: Spying on house appliances with audio sensor nodes. In Proceedings of BuildSys'10. ACM, New York, NY, 31--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Tendril. 2012. The Tendril residential energy ecosystem. http://www.tendrilinc.com/.Google ScholarGoogle Scholar
  23. R. Tibshirani. 1994. Regression shrinkage and selection via the lasso. J. Royal Stat. Soci. Series B 58, 267--288.Google ScholarGoogle Scholar
  24. Y. Wang, X. Hao, L. Song, C. Wu, Y. Wang, C. Hu, and L. Yu. 2012. Tracking states of massive electrical appliances by lightweight metering and sequence decoding. In Proceedings of the 6th International Workshop on SensorKDD'12. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Monitoring massive appliances by a minimal number of smart meters

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!