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SmartM: A Non-intrusive Load Monitoring Platform

Conference paper
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Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 373)

Abstract

Real-time energy consumption monitoring is becoming increasingly important in smart energy management as it provides the opportunity for novel applications through data analytics, including anomaly detection, energy leakage, and theft. This paper presents a smart non-intrusive load monitoring approach for residential households, collecting fine-grained energy consumption data and disaggregating the data of appliances. The paper describes the implementation of the monitoring system, the data set, load disaggregation, and the challenges for future work.

Keywords

Non-intrusive load monitoring Disaggregation Platform Data set 

Notes

Acknowledgements

This research was supported by the Røskilde Smart Monitoring Household Project (No: 82568), and the CITIES project (No: 1035-0027B).

References

  1. 1.
    Kitzing, L., Katz, J., Schroder, S.T., Morthorst, P.E., Andersen, F.M.: The residential electricity sector in Denmark: a description of current conditions. Technical University of Denmark, Lyngby (2016)Google Scholar
  2. 2.
    Ayres, I., Raseman, S., Shih, A.: Evidence from two large field experiments that peer comparison feedback can reduce residential energy usage. J. Law Econ. Organ. 29(5), 992–1022 (2013)CrossRefGoogle Scholar
  3. 3.
    Zeifman, M., Roth, K.: Nonintrusive appliance load monitoring: review and outlook. IEEE Trans. Consum. Electron. 57(1), 76–84 (2011)CrossRefGoogle Scholar
  4. 4.
    Najafi, B., Moaveninejad, S., Rinaldi, F.: Data analytics for energy disaggregation: methods and applications. In: Big Data Application in Power Systems, pp. 377–408 (2018)Google Scholar
  5. 5.
    Iftikhar, N., Pedersen, T.B.: Using a time granularity table for gradual granular data aggregation. In: Catania, B., Ivanović, M., Thalheim, B. (eds.) ADBIS 2010. LNCS, vol. 6295, pp. 219–233. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15576-5_18CrossRefGoogle Scholar
  6. 6.
    Liu, X., Nielsen, P. S., Heller, A., Gianniou, P.: SciCloud: a scientific cloud and management platform for smart city data. In: Proceedings of DEXA Workshop, pp. 27–31 (2017)Google Scholar
  7. 7.
    Apache Zeppelin. https://zeppelin.apache.org/ as of 2019-06-01
  8. 8.
    PostgreSQL. https://www.postgresql.org/ as of 2019-06-01
  9. 9.
    Hellerstein, J.M., et al.: The MADlib analytics library: or MAD skills, the SQL. Proc. VLDB Endow. 5(12), 1700–1711 (2012)CrossRefGoogle Scholar
  10. 10.
    Liu, X., Golab, L., Golab, W., Ilyas, I.F., Jin, S.: Smart meter data analytics: systems, algorithms and benchmarking. ACM Trans. Database Syst. 42(1), 2 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Berges, M.E., Goldman, E., Matthews, H.S., Soibelman, L.: Enhancing electricity audits in residential buildings with nonintrusive load monitoring. J. Ind. Ecol. 14(5), 844–858 (2010)CrossRefGoogle Scholar
  12. 12.
    Chalmers, C., Fergus, P., Montanez, C., Sikdar, S., Ball, F., Kendall, B.: Detecting activities of daily living and routine behaviours in dementia patients living alone using smart meter load disaggregation (2019). arXiv preprint arXiv:1903.12080
  13. 13.
    Tang, G., Ling, Z., Li, F., Tang, D., Tang, J.: Occupancy-aided energy disaggregation. Comput. Netw. 117, 42–51 (2017)CrossRefGoogle Scholar
  14. 14.
    Batra, N., et al.: NILMTK: an open source toolkit for non-intrusive load monitoring. In: Proceedings of the 5th International Conference on Future energy systems, pp. 265–276 (2014)Google Scholar
  15. 15.
    Suzuki, K., Inagaki, S., Suzuki, T., Nakamura, H., Ito, K.: Nonintrusive appliance load monitoring based on integer programming. In: Proceedings of SICE, pp. 2742–2747 (2008)Google Scholar
  16. 16.
    Santin, O.G., Itard, L., Visscher, H.: The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock. Energy Build. 41(11), 1223–1232 (2009)CrossRefGoogle Scholar
  17. 17.
    Kolter, J.Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. In: Proceedings of Workshop on SIGKDD, vol. 25, pp. 59–62 (2011)Google Scholar
  18. 18.
    Makonin, S., Popowich, F., Bartram, L., Gill, B., Bajic, I.V.: AMPds: a public dataset for load disaggregation and eco-feedback research. In: Proceedings of Electrical Power and Energy Conference, pp. 1–6 (2013)Google Scholar
  19. 19.
    Parson, O., et al.: Dataport and NILMTK: a building data set designed for non-intrusive load monitoring. In: Proceedings of GlobalSIP, pp. 210–214 (2015)Google Scholar
  20. 20.
    Batra, N., Parson, O., Berges, M., Singh, A., Rogers, A.: A comparison of non-intrusive load monitoring methods for commercial and residential buildings. arXiv preprint arXiv:1408.6595 (2014)
  21. 21.
    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
  22. 22.
    Kelly, J., et al.: NILMTK v0.2: a non-intrusive load monitoring toolkit for large scale data sets: demo abstract. In: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, pp. 182–183 (2014)Google Scholar
  23. 23.
    Nguyen, T.K., Dekneuvel, E., Jacquemod, G., Nicolle, B., Zammit, O., Nguyen, V.C.: Development of a real-time non-intrusive appliance load monitoring system: an application level model. Int. J. Electr. Power Energy Syst. 90, 168–180 (2017)CrossRefGoogle Scholar
  24. 24.
    Welikala, S., Thelasingha, N., Akram, M., Ekanayake, P.B., Godaliyadda, R.I., Ekanayake, J.B.: Implementation of a robust real-time non-intrusive load monitoring solution. Appl. Energy 238, 1519–1529 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Technical University of DenmarkKongens LyngbyDenmark

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