Abstract
The current European legislation requires households the installation of smart metering systems. These will eventually allow electric utilities to gather richly detailed data of consumption. In this scenario, the implementation of data mining procedures for actionable knowledge extraction could be the key to competitive advantage. These may take the form of market segmentation using clustering techniques for the identification of customer behaviour patterns of electricity consumption that could justify the definition of tailored tariffs. In this brief paper, we show that the combination of a standard clustering algorithm with a similarity measure specifically defined for non-i.i.d. data, namely Dynamic Time Warping, can reveal an actionable segmentation of a real consumer market, combining business criteria and quantitative evaluation.
This is a preview of subscription content, log in via an institution to check access.
References
Orden IET/290/2012, de 16 de febrero, por la que se modifica la Orden ITC/3860/2007, de 28 de diciembre, por la que se revisan las tarifas eléctricas a partir del 1 de enero de 2008 en lo relativo al plan de sustitución de contadores. https://www.boe.es/diario_boe/txt.php?id=BOE-A-2012-2538
The European Parliament and The Council of the European Union: Directive 2006/32/EC of the European Parliament and of the Council on Energy End-Use Efficiency and Energy Service and Repealing Council Directive 93/76/EEC (2006)
Flath, C., Nicolay, D., Conte, T., van Dinther, C., Filipova-Neumann, L.: Cluster analysis of smart metering data. Bus. Inf. Syst. Eng. 4(1), 31–39 (2012)
Alzate, C., Sinn, M.: Improved electricity load forecasting via kernel spectral clustering of smart meters. In: Proceedings of the IEEE 13th International Conference on Data Mining (ICDM 2013), pp. 943–948. IEEE (2013)
Lines, J., Bagnall, A., Caiger-Smith, P., Anderson, S.: Classification of household devices by electricity usage profiles. In: Yin, H., Wang, W., Rayward-Smith, V. (eds.) IDEAL 2011. LNCS, vol. 6936, pp. 403–412. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23878-9_48
Pelleg, D., Moore, A.: X-means: extending K-means with efficient estimation of the number of clusters. In: Langley, P. (ed.) ICML 2000. Morgan Kaufmann, San Francisco, pp. 727–734 (2000)
Paparrizos, J., Gravano, L.: k-Shape: efficient and accurate clustering of time series. SIGMOD Rec. 45(1), 69–76 (2016)
Petitjean, F., Forestier, G., Webb, G.I., Nicholson, A.E., Chen, Y., Keogh, E.: Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm. Knowl. Inf. Syst. 47(1), 1–26 (2016)
Senin, P.: Dynamic time warping algorithm review. Technical report, Information and Computer Science Department, University of Hawaii at Manoa Honolulu, USA, pp. 1–23 (2008)
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop. vol. 10, no. 16 (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Vellido, A., García, D.L. (2017). Electricity Rate Planning for the Current Consumer Market Scenario Through Segmentation of Consumption Time Series. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-65340-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65339-6
Online ISBN: 978-3-319-65340-2
eBook Packages: Computer ScienceComputer Science (R0)