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An Introduction of Non-intrusive Load Monitoring and Its Challenges in System Framework

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Abstract

With the increasing of energy demand and electricity price, researchers gain more and more interest among the residential load monitoring. In order to feed back the individual appliance’s energy consumption instead of the whole-house energy consumption, Non-Intrusive Load Monitoring (NILM) is a good choice for residents to respond the time-of-use price and achieve electricity saving. In this paper, we discuss the system framework of NILM and analyze the challenges in every module. Besides, we study and compare the public data sets and recent approaches to non-intrusive load monitoring techniques.

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References

  1. Sundramoorthy, V., Cooper, G., Linge, N., Liu, Q.: Domesticating energy-monitoring systems: challenges and design concerns. IEEE Pervasive Comput. 10(1), 20–27 (2011)

    Article  Google Scholar 

  2. Alahmad, M.A., Wheeler, P.G., Schwer, A., Eiden, J., Brumbaugh, A.: A comparative study of three feedback devices for residential real-time energy monitoring. IEEE Trans. Ind. Electron. 59(4), 2002–2013 (2012)

    Article  Google Scholar 

  3. Ehrhardt-Martinez, K., Donnelly, K.A., Laitner, S.: Advanced Metering Initiatives and Residential Feedback Programs: A Meta-review for Household Electricity-Saving Opportunities. American Council for an Energy-Efficient Economy, Washington, DC (2010)

    Google Scholar 

  4. Ridi, A., Gisler, C., Hennebert, J.: A survey on intrusive load monitoring for appliance recognition. Biochem. Biophys. Res. Commun. 94(4), 3702–3707 (2014)

    Google Scholar 

  5. Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)

    Article  Google Scholar 

  6. Froehlich, J., Larson, E., Gupta, S., Cohn, G., Reynolds, M.S., Patel, S.N.: Disaggregated end-use energy sensing for the smart grid. IEEE Pervasive Comput. 10(1), 28–39 (2011)

    Article  Google Scholar 

  7. Laughman, C., Lee, D., Cox, R., Shaw, S., Leeb, S., Norford, L., Armstrong, P.: Power signature analysis. IEEE Power Energ. Mag. 1(2), 56–63 (2003)

    Article  Google Scholar 

  8. 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 Delivery 25(2), 551–560 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Zeifman, M., Roth, K.: Nonintrusive appliance load monitoring: review and outlook. IEEE Trans. Consum. Electron. 57(1), 76–84 (2011)

    Article  Google Scholar 

  11. Xuezhi, W., Ling, S., Yu, X., Wei, F.: A rapid learning algorithm for vehicle classification. Inf. Sci. 295(1), 395–406 (2015)

    Google Scholar 

  12. Bin, G., Victor, S.S., Keng, Y.T., Walter, R., Shuo, L.: Incremental support vector learning for ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. 26(7), 1403–1416 (2015)

    Article  MathSciNet  Google Scholar 

  13. Makonin, S., Popowich, F., Bartram, L., Gill, B., Bajic, I.V.: AMPds: a public dataset for load disaggregation and eco-feedback research. In: Proceeding of 2013 IEEE Conference on Electrical Power and Energy (EPEC), pp. 1–6 (2013)

    Google Scholar 

  14. Anderson, K., Ocneanu, A., Benitez, D., Carlson, D., Rowe, A., Berges, M.: BLUED: a fully labeled public dataset for event-based non-intrusive load monitoring research. In: Proceedings of the 2nd KDD Workshop on Data Mining Applications in Sustainability (SustKDD), pp. 1–5 (2012)

    Google Scholar 

  15. Monacchi, A., Egarter, D., Elmenreich, W., D’Alessandro, S., Tonello, A.M.: GREEND: an energy consumption dataset of households in Italy and Austria. In: Proceedings of 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 511–516 (2014)

    Google Scholar 

  16. Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: insights into home energy consumption in India. In: Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings, pp. 1–8 (2013)

    Google Scholar 

  17. Kolter, J.Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. In: Proceedings of the SustKDD Workshop on Data Mining Applications in Sustainability (SustKDD), San Diego, CA, USA, August 2011

    Google Scholar 

  18. Barker, S., Mishra, A., Irwin, D., Cecchet, E., Shenoy, P., Albrecht, J.: Smart*: an open data set and tools for enabling research in sustainable homes. In: Proceedings of the 1st KDD Workshop on Data Mining Applications in Sustainability (SustKDD), San Diego, California, USA, August 2012

    Google Scholar 

  19. 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: Proceedings of the 2nd IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT), October 2012

    Google Scholar 

  20. Kelly, J., Knottenbelt, W.: UK-DALE: a dataset recording UK domestic appliance-level electricity demand and whole-house demand. arXiv, April 2014

    Google Scholar 

  21. Jian, S., Haowen, T., Jin, W., Jinwei, W., Sungyoung, L.: A novel routing protocol providing good transmission reliability in underwater sensor networks. J. Internet Technol. 16(1), 171–178 (2015)

    Google Scholar 

  22. Zeifman, M.: Disaggregation of home energy display data using probabilistic approach. IEEE Trans. Consum. Electron. 58(1), 23–31 (2012)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work is supported by the NSFC (61300238, 61300237, 61232016, 1405254, 61373133), Marie Curie Fellowship (701697-CAR-MSCA-IFEF-ST), Basic Research Programs (Natural Science Foundation) of Jiangsu Province (BK20131004) and the PAPD fund.

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Correspondence to Qi Liu .

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Liu, Q., Lu, M., Liu, X., Linge, N. (2016). An Introduction of Non-intrusive Load Monitoring and Its Challenges in System Framework. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_23

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  • DOI: https://doi.org/10.1007/978-3-319-48674-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48673-4

  • Online ISBN: 978-3-319-48674-1

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