Accurate models for predicting generation, demand, prices, and storage under uncertainties are essential for managing safe, sustainable and reliable electric grids. In this investigation, the use of data-mining methods for building models of electrical consumption and renewable generation aimed at integrating renewable generation for smart grid control is studied. The results presented are part of the I3RES project that aims at building future energy solutions for smart electric grids considering the typical uncertainties of the renewable generation. Our results indicate that the data-mining techniques are able to provide forecasts with reasonable accuracy in the presence of uncertainties. Furthermore, such forecasts are useful in building controllers that can perform control actions such as demand side management in smart grids.
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