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Generating Load Profiles Using Smart Metering Time Series

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Advances in Fuzzy Logic and Technology 2017 (EUSFLAT 2017, IWIFSGN 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 641))

Abstract

In this work we present a practice-oriented approach for generating load profiles as a means to forecast energy demand by using smart metering time series. The general idea is to apply fuzzy clustering on historic consumption time series. The segmentation yielded helps electricity companies to identify customers with similar consumption behavior. This knowledge can be used to plan available energy capacities in advance. What makes this approach special is that this approach segments consumption time series by time in addition to identifying customer groups. This is done not only to accommodate for customers potentially behaving completely different on working days than on local holidays for example, but also to build the resulting load profiles in a way the electricity companies can adapt with minimal adjustments. We also evaluate our approach using two real world smart metering datasets and discuss potential improvements.

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References

  1. BTU EVU Beratung GmbH. http://www.btu-evu.de

  2. Andersen, F., Larsen, H., Boomsma, T.: Long-term forecasting of hourly electricity load: identification of consumption profiles and segmentation of customers. Energ. Convers. Manag. 68, 244–252 (2013)

    Article  Google Scholar 

  3. Beckel, C., Sadamori, L., et al.: Revealing household characteristics from smart meter data. Energy 78, 397–410 (2014)

    Article  Google Scholar 

  4. Benítez, I., Quijano, A., et al.: Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers. Int. J. Electr. Power Energ. Syst. 55, 437–448 (2014)

    Article  Google Scholar 

  5. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, New York (1981)

    Book  MATH  Google Scholar 

  6. Bock, C.: Clustering-Ansatz zur Erstellung von Lastprofilen zur Vorhersage des Stromverbrauchs. In: Proceedings of the 28th GI-Workshop Grundlagen von Datenbanken (GvDB 2016), pp. 21–26 (2016)

    Google Scholar 

  7. Bouguessa, M., Wang, S., Sun, H.: An objective approach to cluster validation. Pattern Recogn. Lett. 27(13), 1419–1430 (2006)

    Article  Google Scholar 

  8. CER – The Commission for Energy Regulation. Accessed via the Irish social science data archive. http://www.ucd.ie/issda

  9. Chicco, G., Ilie, I.S.: Support vector clustering of electrical load pattern data. IEEE Trans. Power Syst. 24(3), 1619–1628 (2009)

    Article  Google Scholar 

  10. Diamantoulakis, P.D., Kapinas, V.M., Karagiannidis, G.K.: Big data analytics for dynamic energy management in smart grids (2015). CoRR, abs/1504.02424

    Google Scholar 

  11. Figueiredo, V., Rodrigues, F., et al.: An electric energy consumer characterization framework based on data mining techniques. IEEE Trans. Power Syst. 20(2), 596–602 (2005)

    Article  Google Scholar 

  12. Fusco, F., Wurst, M., Yoon, J.: Mining residential household information from low-resolution smart meter data. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3545–3548, November 2012

    Google Scholar 

  13. Gath, I., Geva, A.: Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 11, 773–780 (1989)

    Article  MATH  Google Scholar 

  14. Hathaway, R.J., Bezdek, J.C.: Fuzzy c-means clustering of incomplete data. IEEE Trans. Syst. Man Cybern. Part B Cybern. 31–35, 735–744 (2001). IEEE

    Google Scholar 

  15. Hayn, M., et al.: Electricity load profiles in Europe: the importance of household segmentation. Energ. Res. Soc. Sci. 3, 30–45 (2014)

    Article  Google Scholar 

  16. Hernández, L., Baladrón, C., et al.: Classification and clustering of electricity demand patterns in industrial parks. Energies 5(12), 5215 (2012)

    Article  Google Scholar 

  17. Himmelspach, L.: Fuzzy clustering of incomplete data. Ph.D. thesis, Heinrich-Heine-University, Institute of Computer Science (2016)

    Google Scholar 

  18. Kolo, A., Kretschmann, C.: Hebung finanzieller Potentiale in der Strombilanzierung. et – Energiewirtschaftliche Tagesfragen 7, 43–45 (2015)

    Google Scholar 

  19. Yang, S.L., Shen, C., et al.: A review of electric load classification in smart grid environment. Renew. Sustain. Energ. Rev. 24, 103–110 (2013)

    Google Scholar 

  20. López, J.J., Aguado, J.A., et al.: Hopfield-k-means clustering algorithm: a proposal for the segmentation of electricity customers. Electr. Power Syst. Res. 81(2), 716–724 (2011)

    Article  Google Scholar 

  21. Mahmoudi-Kohan, N., Moghaddam, M.P., Sheikh-El-Eslami, M.: An annual framework for clustering-based pricing for an electricity retailer. Electr. Power Syst. Res. 80(9), 1042–1048 (2010)

    Article  Google Scholar 

  22. Misiti, M., Misiti, Y., et al.: Optimized clusters for disaggregated electricity load forecasting. Revstat 8(2), 105–124 (2010)

    MathSciNet  MATH  Google Scholar 

  23. Rodrigues, F., Duarte, J., et al.: Proceedings of Machine Learning and Data Mining in Pattern Recognition: Third International Conference, MLDM 2003, Leipzig, Germany, 5–7 July 2003, pp. 73–85. Springer (2003)

    Google Scholar 

  24. Rudin, C., Waltz, D., et al.: Machine learning for the New York city power grid. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 328–345 (2012)

    Article  Google Scholar 

  25. Räsänen, T., Voukantsis, D., et al.: Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data. Appl. Energ. 87(11), 3538–3545 (2010)

    Article  Google Scholar 

  26. Schäfer, H., et al.: Analysing the segmentation of energy consumers using mixed fuzzy clustering. In: Fuzzy Systems (FUZZ-IEEE), pp. 1–7, August 2015

    Google Scholar 

  27. Viegas, J.P.L., Vieira, S.M., Sousa, J.M.C.: Fuzzy clustering and prediction of electricity demand based on household characteristics. In: 2015 Conference on International Fuzzy Systems Association and European Society Fuzzy Logic and Technolgy (IFSA-EUSFLAT-15)

    Google Scholar 

  28. von Roon, D.-I.S., et al.: Statusbericht zum Standardlastprofilverfahren Gas, 11 2014. https://www.ffegmbh.de/kompetenzen/system-markt-analysen/508-statusbericht-standardlastprofile-gas

  29. Zhang, X., Sun, C.: Dynamic intelligent cleaning model of dirty electric load data. Energ. Conver. Manag. 49(4), 564–569 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Heinrich-Heine-University, 40225, Düsseldorf, Germany

    Christian Bock

  2. BTU EVU Beratung GmbH, 40545, Düsseldorf, Germany

    Christian Bock

Authors
  1. Christian Bock
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Correspondence to Christian Bock .

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Editors and Affiliations

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

    Janusz Kacprzyk

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

    Eulalia Szmidt

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

    Sławomir Zadrożny

  4. Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Krassimir T. Atanassov

  5. WIT - Warsaw School of Information Technology, Warsaw, Poland

    Maciej Krawczak

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Bock, C. (2018). Generating Load Profiles Using Smart Metering Time Series. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-319-66830-7_20

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  • DOI: https://doi.org/10.1007/978-3-319-66830-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66829-1

  • Online ISBN: 978-3-319-66830-7

  • eBook Packages: EngineeringEngineering (R0)

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