A Hybrid Ensemble Model Applied to the Short-Term Load Forecasting Problem

In this paper we present a methodology based on a combination of many distinct predictors in an ensemble, named hybrid ensemble model, to obtain a more accurate output using the results of single predictors. As basic components, we have used artificial neural networks and support vector machines models. In order to evaluate the performance, the hybrid model was required to predict a 24 h daily series energy consumption of a Brazilian electrical operation unit located in the northeast of Brazil. The proposed ensemble model has reached an error 25% smaller than that achieved by the best single predictor. The model was initialized several times to confirm that ensembles of predictors also tend to produce low variance profiles.