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Real-Time Event Detection for Energy Data Streams

  • Aqeel H. KazmiEmail author
  • Michael J. O’Grady
  • Gregory M. P. O’Hare
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8850)

Abstract

Appliance specific energy monitoring is perceived as a prerequisite for reducing energy usage in households. A number of approaches exist, however, Non-Intrusive appliance Load Monitoring is considered to be the most promising and scalable method. This method can also facilitate Ambient Intelligent applications with the hope activity recognition of the resident is of paramount importance. In this paper, we propose an event detection algorithm to support non-intrusive energy monitoring. A performance evaluation of this algorithm has been carried out on a reference dataset.

Keywords

Non-Intrusive Appliance Load Monitoring Event Detection 

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References

  1. 1.
    Alasalmi, T., Suutala, J., Rning, J.: Real-time non-intrusive appliance load monitor - feedback system for single-point per appliance electricity usage. In: SmartGreens, pp. 203–208. SciTePress (2012)Google Scholar
  2. 2.
    Anderson, K., Berges, M., Ocneanu, A., Benitez, D., Moura, J.: Event detection for non intrusive load monitoring. In: IECON 2012 – 38th Annual Conference on IEEE Industrial Electronics Society, pp. 3312–3317 (October 2012)Google Scholar
  3. 3.
    Armel, C.K., Gupta, A., Shrimali, G., Albert, A.: Is disaggregation the holy grail of energy efficiency? the case of electricity. Energy Policy 52(C), 213–234 (2013)Google Scholar
  4. 4.
    Berges, M., Goldman, E., Matthews, H., Soibelman, L., Anderson, K.: User-centered nonintrusive electricity load monitoring for residential buildings. Journal of Computing in Civil Engineering 25(6), 471–480 (2011)CrossRefGoogle Scholar
  5. 5.
    Fischer, C.: Feedback on household electricity consumption: A tool for saving energy? Energy Efficiency 1, 79–104 (2008)CrossRefGoogle Scholar
  6. 6.
    Hart, G.W.: Nonintrusive appliance load monitoring. Proceedings of the IEEE 80(12) (1992)Google Scholar
  7. 7.
    Jin, Y., Tebekaemi, E., Berges, M., Soibelman, L.: Robust adaptive event detection in non-intrusive load monitoring for energy aware smart facilities. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4340–4343 (May 2011)Google Scholar
  8. 8.
    Kazmi, A.H., O’Grady, M.J., Delaney, D.T., Ruzzelli, A.G., O’Hare, G.M.P.: A review of wireless-sensor-network-enabled building energy management systems. ACM Trans. Sen. Netw. 10(4), 66:1–66:43 (2014)Google Scholar
  9. 9.
    Kolter, J.Z., Johnson, M.J.: REDD: A Public Data Set for Energy Disaggregation Research. In: SustKDD Workshop on Data Mining Applications in Sustainability (2011)Google Scholar
  10. 10.
    Lillis, D., O’Sullivan, T., Holz, T., Muldoon, C., O’Grady, M., O’Hare, G.: Smart home energy management. Recent advances in ambient intelligence and context-aware computing. IGI Global (in press)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Aqeel H. Kazmi
    • 1
    Email author
  • Michael J. O’Grady
    • 1
  • Gregory M. P. O’Hare
    • 1
  1. 1.School of Computer Science and InformaticsUniversity College DublinDublin 4Ireland

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