Evolving RBF predictive models to forecast the Portuguese electricity consumption

Abstract Abstract The Portuguese power grid company wants to improve the accuracy of the electricity load demand (ELD) forecast within an horizon of 24 to 48 hours, in order to identify the need of reserves to be allocated in the Iberian Market. In this work we present some preliminary results about the identification of radial basis function (RBF) neural network (NN) ELD predictive models and about the performance of a model selection algorithm. The methodology follows the principles already employed by the authors in different applications: the NN models are trained by the Levenberg-Marquardt algorithm using a modified training criterion, and the model structure (number of neurons and input terms) is evolved using a Multi-Objective Genetic Algorithm (MOGA). The set of goals and objectives used in the MOGA model optimisation reflect different requirements in the design: obtaining good generalisation ability, good balance between one-step-ahead prediction accuracy and model complexity, and good multi-step prediction accuracy. A number of experiments were carried out, whose results are presented, producing already a number of models whose predictive performance is satisfactory.