Abstract
Current demands on the energy market, such as legal policies towards green energy usage and economic pressure due to growing competition, require energy companies to increase their understanding of consumer behavior and streamline business processes. One way to help achieve these goals is by making use of the increasing availability of smart metering time series. In this paper we extend an approach based on fuzzy clustering using smart meter data to yield load profiles which can be used to forecast the energy demand of customers. In addition, our approach is built with existing business processes in mind. This helps not only to accurately satisfy real world requirements, but also to ease adoption by the industry. We also assess the quality of our approach using real world smart metering datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
BTU EVU Beratung GmbH. http://www.btu-evu.de
Andersen, F., Larsen, H., Boomsma, T.: Long-term forecasting of hourly electricity load: identification of consumption profiles and segmentation of customers. Energy Convers. Manag. 68, 244–252 (2013)
Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, Philadelphia, PA, USA, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)
Beckel, C., Sadamori, L., et al.: Revealing household characteristics from smart meter data. Energy 78, 397–410 (2014)
Benítez, I., Quijano, A., et al.: Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers. Int. J. Electr. Power Energy Syst. 55, 437–448 (2014)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer US, New York (1981). https://doi.org/10.1007/978-1-4757-0450-1
Bock, C.: Generating load profiles using smart metering time series. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K.T., Krawczak, M. (eds.) IWIFSGN/EUSFLAT 2017. AISC, vol. 641, pp. 211–223. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-66830-7_20
Bouguessa, M., Wang, S., Sun, H.: An objective approach to cluster validation. Pattern Recogn. Lett. 27(13), 1419–1430 (2006)
CER - The Commission for Energy Regulation: Accessed via the Irish social science data archive. http://www.ucd.ie/issda
Diamantoulakis, P.D., Kapinas, V.M., Karagiannidis, G.K.: Big data analytics for dynamic energy management in smart grids. CoRR, abs/1504.02424 (2015)
Edelmann, D.H., Kästner, T.: Kosten-Nutzen-Analyse für einen flächendeckenden Einsatz intelligenter Zähler, July 2013. https://www.bmwi.de/Redaktion/DE/Publikationen/Studien/kosten-nutzen-analyse-fuer-flaechendeckenden-einsatz-intelligenterzaehler.html
Fernandes, M.P., Viegas, J.L., Vieira, S.M., Sousa, J.M.C.: Seasonal clustering of residential natural gas consumers. In: Carvalho, J.P., Lesot, M.-J., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2016. CCIS, vol. 610, pp. 723–734. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40596-4_60
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
Hathaway, R.J., Bezdek, J.C.: Fuzzy c-means clustering of incomplete data. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 31(5), 735–744 (2001)
Hayn, M., et al.: Electricity load profiles in Europe: the importance of household segmentation. Energy Res. Soc. Sci. 3, 30–45 (2014)
Hernández, L., Baladrón, C., et al.: Classification and clustering of electricity demand patterns in industrial parks. Energies 5(12), 5215 (2012)
Hoffman, P., Frey, G., Friedrich, D.M., Kerber-Clasen, S., Marschall, J., Geiger, D.M.: Praxistest “Moderne Energiesparsysteme im Haushalt” (2012)
Kolo, A., Kretschmann, C.: Hebung finanzieller Potentiale in der Strombilanzierung. et - Energiewirtschaftliche Tagesfragen 7, 43–45 (2015)
Zhou, K., Yang, S., et al.: A review of electric load classification in smart grid environment. Renew. Sustain. Energy Rev. 24, 103–110 (2013)
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)
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)
Viegas, J.P.L., Vieira, S.M., Sousa, J.M.C.: Fuzzy clustering and prediction of electricity demand based on household characteristics. In: 2015 World Congress of the International Fuzzy Systems Association and Conference of the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT 2015) (2015)
von Roon, D.-I.S., et al.: Statusbericht zum Standardlastprofilverfahren Gas, November 2014. https://www.ffegmbh.de/kompetenzen/system-markt-analysen/508-statusbericht-standardlastprofile-gas
Zhang, X., Sun, C.: Dynamic intelligent cleaning model of dirty electric load data. Energy Convers. Manag. 49(4), 564–569 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Bock, C. (2018). Forecasting Energy Demand by Clustering Smart Metering Time Series. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-91473-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-91473-2_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91472-5
Online ISBN: 978-3-319-91473-2
eBook Packages: Computer ScienceComputer Science (R0)