Novel method for typical load curves characterization of industrial consumers towards the smart grids

With the advent of smart grids, the volume of data from metering and information about consumers to be storaged and analysed by the distribution companies will be very large, mainly due to the use of advanced metering infrastructure and digital meters with automatic readers. This new scenario has stimulated these companies to develop intelligent computing systems which can handle data and information about their consumers, generating a deeper knowledge about their habits and dynamics of energy consumption, their typical load curve typology and the main factors that influence their demand. In this sense, Data Mining (DM) techniques should constitute an important tool for the generation of new and useful information for the utility. This work presents significative advances in the usual procedures of characterization of daily typical load curves of the electrical energy consumers, focusing on the industrial consumers. The tasks of clustering, search for association and classification rules of typologies were implemented based on data from metering and responses to questionnaires, involving functional aspects. It is proposed in this paper a more precise way to estimate the daily load curves of industrial consumers, improving the activities associated with the expansion planning process of the distribution utilities.