Electricity Rate Planning for the Current Consumer Market Scenario Through Segmentation of Consumption Time Series

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.