Unseen Appliances Identification

  • Antonio Ridi
  • Christophe Gisler
  • Jean Hennebert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)


We assess the feasibility of unseen appliance recognition through the analysis of their electrical signatures recorded using low-cost smart plugs. By unseen, we stress that our approach focuses on the identification of appliances that are of different brands or models than the one in training phase. We follow a strictly defined protocol in order to provide comparable results to the scientific community. We first evaluate the drop of performance when going from seen to unseen appliances. We then analyze the results of different machine learning algorithms, as the k-Nearest Neighbor (k-NN) and Gaussian Mixture Models (GMMs). Several tunings allow us to achieve 74% correct accuracy using GMMs which is our current best system.


Intrusive Load Monitoring (ILM) appliance recognition electric signatures load identification 


  1. 1.
    Chetty, M., Tran, D., Grinter, R.E.: Getting to green: understanding resource consumption in the home. In: Proc. UbiComp 2008, pp. 242–251 (2008)Google Scholar
  2. 2.
    Rahimi, S., Chan, A.D.C., Goubran, R.A.: Usage monitoring of electrical devices in a smart home. In: Proc. IEEE EMBS 2011 (2011)Google Scholar
  3. 3.
    U.S. Household Electricity Report, E.I.A. (2005),
  4. 4.
    Hart, G.W.: Nonintrusive appliance load monitoring. Proceedings of the IEEE 80, 1870–1891 (1992)CrossRefGoogle Scholar
  5. 5.
    Clement, K., Pardon, I., Driesen, J.: Standby power consumption in belgium. In: Proc. EPQU 2007 (2007)Google Scholar
  6. 6.
    Guan, L., Berrill, T., Brown, R.J.: Measurement of standby power for selected electrical appliances in australia. Energy and Buildings 43, 485–490 (2011)CrossRefGoogle Scholar
  7. 7.
    Zoha, A., Gluhak, A., Imran, M.A., Rajasegarar, S.: Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors, 16838–16866 (2012)Google Scholar
  8. 8.
    Adeel Abbas Zaidi, F.K., Palensky, P.: Load recognition for automated demand response in microgrids. In: Proc. IECON 2010 (2010)Google Scholar
  9. 9.
    Reinhardt, A., Baumann, P., Burgstahler, D., Hollick, M., Chonov, H., Werner, M., Steinmetz, R.: On the accuracy of appliance identification based on distributed load metering data. In: Proc. SustainIT 2012 (2012)Google Scholar
  10. 10.
    Zufferey, D., Gisler, C., Khaled, O.A., Hennebert, J.: Machine learning approaches for electric appliance classification. In: Proc. ISSPA 2012 (2012)Google Scholar
  11. 11.
    Ridi, A., Gisler, C., Hennebert, J.: Automatic identification of electrical appliances using smart plugs. In: Proc. Wosspa 2013 (2013)Google Scholar
  12. 12.
    Gisler, C., Ridi, A., Zufferey, D., Khaled, O.A., Hennebert, J.: Appliance consumption signature database and recognition test protocols. In: Proc. Wosspa 2013 (2013)Google Scholar
  13. 13.
    Hennebert, J.: Hidden Markov models and artificial neural networks for speech and speaker recognition. PhD thesis (1998)Google Scholar
  14. 14.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. Journal of Royal Statistical Society 39, 1–38 (1977)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Antonio Ridi
    • 1
    • 2
  • Christophe Gisler
    • 1
    • 2
  • Jean Hennebert
    • 1
    • 2
  1. 1.College of Engineering and Architecture of Fribourg, ICT InstituteUniversity of Applied Sciences Western SwitzerlandSwitzerland
  2. 2.Department of InformaticsUniversity of FribourgFribourgSwitzerland

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