HMM-based Transient and Steady-state Current Signals Modeling for Electrical Appliances Identification

Mohamed Nait-Meziane, Abdenour Hacine-Gharbi, Philippe Ravier, Guy Lamarque, Jean-Charles Le Bunetel, Yves Raingeaud

Abstract

The electrical appliances identification problem is gaining a rapidly growing interest these past few years due to the recent need of this information in the new smart grid configuration. In this work, we propose to construct an appliance identification system based on the use of Hidden Markov Models (HMM) to model transient and steady-state electrical current signals. For this purpose, we investigate the usefulness of different choices for the proposed identification system such as: the use of the transient and the steady-state current signals, the use of even and odd-order harmonics as features, and the optimal number of features to take into account. This work also discusses the choice of the Short-Time Fourier Series (STFS) coefficients as adapted features for the representation of transient and steady-state current signals.

References

  1. Baranski, M. and Voss, J. (2003). Nonintrusive appliance load monitoring based on an optical sensor. In Power Tech Conference Proceedings, 2003 IEEE Bologna, volume 4, pages 8-pp. IEEE.
  2. Beckel, C., Kleiminger, W., Staake, T., and Santini, S. (2012). Improving device-level electricity consumption breakdowns in private households using on/off events. ACM SIGBED Review, 9(3):32-38.
  3. Burbano Acu n˜a, M. D. (2015). Intrusive and non-intrusive load monitoring (a survey). Latin-American Journal of Computing, Systems Engineering, National Polytechnic School, Ecuador, 2(1).
  4. Carrie Armel, K., Gupta, A., Shrimali, G., and Albert, A. (2013). Is disaggregation the holy grail of energy efficiency? the case of electricity. Energy Policy, 52:213- 234.
  5. Chakravarty, P. and Gupta, A. (2013). Impact of energy disaggregation on consumer behavior. In UC Berkeley: Behavior, Energy and Climate Change Conference.
  6. Chan, W., So, A. T., and Lai, L. (2000). Harmonics load signature recognition by wavelets transforms. In Electric Utility Deregulation and Restructuring and Power Technologies, 2000. Proceedings. DRPT 2000. International Conference on, pages 666-671. IEEE.
  7. Chang, H.-H., Lin, C.-L., and Yang, H.-T. (2008). Load recognition for different loads with the same real power and reactive power in a non-intrusive loadmonitoring system. In Computer Supported Cooperative Work in Design, 2008. CSCWD 2008. 12th International Conference on, pages 1122-1127. IEEE.
  8. Cole, A. I. and Albicki, A. (1998). Algorithm for nonintrusive identification of residential appliances. In Circuits and Systems, 1998. ISCAS'98. Proceedings of the 1998 IEEE International Symposium on, volume 3, pages 338-341. IEEE.
  9. Darby, S. (2006). The effectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering, Billing and direct Displays, 486:2006.
  10. Darby, S. (2010). Smart metering: what potential for householder engagement? Building Research & Information, 38(5):442-457.
  11. Drenker, S. and Kader, A. (1999). Nonintrusive monitoring of electric loads. Computer Applications in Power, IEEE, 12(4):47-51.
  12. Du, Y., Du, L., Lu, B., Harley, R., and Habetler, T. (2010). A review of identification and monitoring methods for electric loads in commercial and residential buildings. In Energy Conversion Congress and Exposition (ECCE), 2010 IEEE, pages 4527-4533. IEEE.
  13. Feinberg, E. A. and Genethliou, D. (2005). Load forecasting. In Applied mathematics for restructured electric power systems, pages 269-285. Springer.
  14. Fischer, C. (2008). Feedback on household electricity consumption: a tool for saving energy? Energy efficiency, 1(1):79-104.
  15. Gao, J., Giri, S., Kara, E. C., and Bergés, M. (2014). Plaid: A public dataset of high-resolution electrical appliance measurements for load identification research: Demo abstract. In Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, BuildSys 7814, pages 198-199, New York, NY, USA. ACM.
  16. Gellings, C. W. (2009). The smart grid: enabling energy efficiency and demand response. The Fairmont Press, Inc.
  17. Hacine-Gharbi, A., Ravier, P., Harba, R., and Mohamadi, T. (2012). Low bias histogram-based estimation of mutual information for feature selection. Pattern Recognition Letters, 33(10):1302 - 1308.
  18. Hancke, G. P., Hancke Jr, G. P., et al. (2012). The role of advanced sensing in smart cities. Sensors, 13(1):393- 425.
  19. Hart, G. (1989). Residential energy monitoring and computerized surveillance via utility power flows. Technology and Society Magazine, IEEE, 8(2).
  20. Hart, G. (1992). Nonintrusive appliance load monitoring. Proc. of the IEEE, 80(12):1870-1891.
  21. Jain, A. K., Duin, R. P. W., and Mao, J. (2000). Statistical pattern recognition: a review. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(1):4- 37.
  22. Lai, P.-h., Trayer, M., Ramakrishna, S., and Li, Y. (2012). Database establishment for machine learning in nilm. In Proceedings of the 1st International Non-Intrusive Load Monitoring Workshop.
  23. Laughman, C., Lee, K., Cox, R., Shaw, S., Leeb, S., Norford, L., and Armstrong, P. (2003). Power signature analysis. Power and Energy Magazine, IEEE, 1(2):56-63. Read.
  24. Leeb, S., Shaw, S., and Jr, J. K. (1995). Transient event detection in spectral envelope estimates for nonintrusive load monitoring. Power Delivery, IEEE Transactions on, 10(3):1200-1210.
  25. Mallat, S. (1999). A wavelet tour of signal processing. Academic Press.
  26. Nait Meziane, M., Ravier, P., Lamarque, G., Abed-Meraim, K., Le Bunetel, J.-C., and Raingeaud, Y. (2015). Modeling and estimation of transient current signals. In Signal Processing Conference (EUSIPCO), 2015 Proceedings of the 23rd European, pages 2005-2009.
  27. Najmeddine, H., El Khamlichi Drissi, K., Pasquier, C., Faure, C., Kerroum, K., Diop, A., Jouannet, T., and Michou, M. (2008). State of art on load monitoring methods. In Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International, pages 1256- 1258. IEEE. Read.
  28. Parson, O. (2012). Using hidden markov model variants for non-intrusive appliance load monitoring from smart meter data.
  29. Parson, O., Ghosh, S., Weal, M., and Rogers, A. (2014). An unsupervised training method for non-intrusive appliance load monitoring. Artificial Intelligence.
  30. Patel, S. N., Robertson, T., Kientz, J. A., Reynolds, M. S., and Abowd, G. D. (2007). At the flick of a switch: Detecting and classifying unique electrical events on the residential power line (nominated for the best paper award). In UbiComp 2007: ubiquitous computing, pages 271-288. Springer. Read.
  31. PLAID (2015). PLAID: the Plug Load Appliance Identification Dataset. [Online] Available from: http://plaidplug.com. [Accessed: 22nd October 2015].
  32. Ridi, A. and Hennebert, J. (2014). Hidden markov models for ilm appliance identification. Procedia Computer Science, 32:1010-1015.
  33. Sultanem, F. (1991). Using appliance signatures for monitoring residential loads at meter panel level. Power Delivery, IEEE Transactions on, 6(4):1380-1385.
  34. Tang, G., Wu, K., and Lei, J. (2014). Semi-intrusive load monitoring for large-scale appliances. In International Workshop on Non-Intrusive Load Monitoring (NILM 2014).
  35. Thiruvaran, T., Phung, T., and Ambikairajah, E. (2013). Automatic identification of electric loads using switching transient current signals. In TENCON Spring Conference, 2013 IEEE, pages 252-256. IEEE.
  36. Ting, K., Lucente, M., Fung, G. S., Lee, W., and Hui, S. (2005). A taxonomy of load signatures for singlephase electric appliances. In IEEE PESC (Power Electronics Specialist Conference), pages 12-18. Read.
  37. Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., and Woodland, P. (2009). The HTK book (for HTK Version 3.4).
  38. Zeifman, M. and Roth, K. (2011). Nonintrusive appliance load monitoring: Review and outlook. Consumer Electronics, IEEE Transactions on, 57(1):76- 84. Read.
  39. Zia, T., Bruckner, D., and Zaidi, A. (2011). A hidden markov model based procedure for identifying household electric loads. In IECON 2011-37th Annual Conference on IEEE Industrial Electronics Society, pages 3218-3223. IEEE.
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Paper Citation


in Harvard Style

Nait-Meziane M., Hacine-Gharbi A., Ravier P., Lamarque G., Le Bunetel J. and Raingeaud Y. (2016). HMM-based Transient and Steady-state Current Signals Modeling for Electrical Appliances Identification . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 670-677. DOI: 10.5220/0005759506700677


in Bibtex Style

@conference{icpram16,
author={Mohamed Nait-Meziane and Abdenour Hacine-Gharbi and Philippe Ravier and Guy Lamarque and Jean-Charles Le Bunetel and Yves Raingeaud},
title={HMM-based Transient and Steady-state Current Signals Modeling for Electrical Appliances Identification},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={670-677},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005759506700677},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - HMM-based Transient and Steady-state Current Signals Modeling for Electrical Appliances Identification
SN - 978-989-758-173-1
AU - Nait-Meziane M.
AU - Hacine-Gharbi A.
AU - Ravier P.
AU - Lamarque G.
AU - Le Bunetel J.
AU - Raingeaud Y.
PY - 2016
SP - 670
EP - 677
DO - 10.5220/0005759506700677