Skip to main content
SpringerLink
Log in

Load Signatures Identification Based on Real Power Fluctuations

  • Conference paper
  • First Online:
International Conference on Information Technology and Communication Systems (ITCS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 640))

Abstract

Identification of individual sources of energy consumption is one new research area that contributes to reduce electricity consumption in buildings, generate energy awareness and improve efficiency of available energy resources usage. This paper proposes a solution based on Multilayer Perceptron and the change of real power to distinguish and identify appliances from the raw load signatures directly without features extraction. Our simulation indicates that these raw load signatures can offer a quick and accurate identification.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ziefman, M., Roth, K.: Non-intrusive appliance load monitoring: Review and outlook. IEEE Trans. Consum. Electron. 57(1), 76–84 (2011)

    Article  Google Scholar 

  2. 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: Sustainable Internet and ICT for Sustainability (SustainIT), pp. 1–9. IEEE (2012)

    Google Scholar 

  3. Ridi, A., Hennebert, J.: Hidden Markov models for ILM appliance identification. In: Proceedings of the International Workshop on Enabling ICT for Smart Buildings (ICT-SB 2014) (2014)

    Google Scholar 

  4. Liang, J., Ng, S.K.K., Kendall, G., Cheng, J.W.M.: Load signature study—part I: basic concept, structure, and methodology. IEEE Trans. Power Deliv 25(2), 551–560 (2010)

    Article  Google Scholar 

  5. Sultanem, F.: Using appliance signatures for monitoring residential loads at meter panel level. IEEE PES Winter Meeting, No. 91 WM 0166 PWRD, New York (1991)

    Google Scholar 

  6. Esa, N.F., Abdullah, M.P., Hassan, M.Y.: A review disaggregation method in non-intrusive appliance load monitoring. Renew. Sustain. Energy Rev. 66, 163–173 (2016)

    Article  Google Scholar 

  7. Zoha, A., Gluhak, A., Imran, M.A., Rajasegarar, S.: Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors 12(12), 16838–16866 (2012)

    Article  Google Scholar 

  8. Hart, G.W.: Non-intrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)

    Article  Google Scholar 

  9. Robertson, D.C., Camps, O.I., Mayer, J.S., Gish Sr., W.B.: Wavelets and electromagnetic power system transients. IEEE Trans. Power Deliv. 11(2), 1050–1056 (1996)

    Article  Google Scholar 

  10. Cole, A.I., Albicki, A.: Data extraction for effective non-intrusive identification of residential power loads. In: Proceedings of IEEE Instrumentation and Measurement Technology Conference, pp. 812–815 (1998)

    Google Scholar 

  11. Cole, A.I., Albicki, A.: Algorithm for non-intrusive identification of residential appliances. In: Proceedings of IEEE International Symposium on Circuits and Systems, pp. 338–341 (1998)

    Google Scholar 

  12. Bouhouras, A.S., Milioudis, A.N., Labridis, D.P.: Development of distinct load signatures for higher efficiency of NILM algorithms. Electr. Power Syst. Res. 117, 163–171 (2014)

    Article  Google Scholar 

  13. Porwik, P., Doroz, R., Orczyk, T.: Signatures verification based on PNN classifier optimised by PSO algorithm. Pattern Recogn. 60, 998–1014 (2016)

    Article  Google Scholar 

  14. Lin, S., Zhao, L., Li, F., Liu, Q., Li, D., Fu, Y.: A nonintrusive load identification method for residential applications based on quadratic programming. Electr. Power Syst. Res. 133, 241–248 (2016)

    Article  Google Scholar 

  15. George, D., Swan, L.G.: A method for distinguishing appliance, lighting and plug load profiles from electricity ‘smart meter’ datasets. Energy and Buildings (2016)

    Google Scholar 

  16. Belley, C., Gaboury, S., Bouchard, B.: AbdenourBouzouane, An efficient and inexpensive method for activity recognition within a smart home based on load signatures of appliances. Pervasive Mob. Comput. 12, 58–78 (2014)

    Article  Google Scholar 

  17. Kwak, N., Choi, C.H.: Input feature selection for classification problems. IEEE Trans. Neural Netw. 13(1), 143–159 (2002)

    Article  Google Scholar 

  18. Srinivasan, D., Ng, W.S., Liew, A.C.: Neural-network-based signature recognition for harmonic source identification. IEEE Trans. Power Deliv. 21(1), 398–405 (2006)

    Article  Google Scholar 

  19. Chang, H.H., Lin, L.S., Chen, N., Lee, W.J.: Particle-swarm-optimization-based nonintrusive demand monitoring and load identification in smart meters. IEEE Trans. Ind. Appl. 49(5), 2229–2236 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mohammadia School of Engineering, Mohammed V University, Rabat, Morocco

    El Bouazzaoui Cherraqi & Abdelilah Maach

Authors
  1. El Bouazzaoui Cherraqi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelilah Maach
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to El Bouazzaoui Cherraqi .

Editor information

Editors and Affiliations

  1. ENSA, Khouribga, Morocco

    Gherabi Noreddine

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cherraqi, E.B., Maach, A. (2018). Load Signatures Identification Based on Real Power Fluctuations. In: Noreddine, G., Kacprzyk, J. (eds) International Conference on Information Technology and Communication Systems. ITCS 2017. Advances in Intelligent Systems and Computing, vol 640. Springer, Cham. https://doi.org/10.1007/978-3-319-64719-7_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64719-7_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64718-0

  • Online ISBN: 978-3-319-64719-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation