A hybrid neuro-evolutionary algorithm for wind power ramp events detection

Abstract

In this work, a hybrid system for wind power ramps events prediction in wind farms is proposed. The system is based on modelling the prediction problem as a binary classification problem from atmospheric reanalysis data inputs. On the other hand, a hybrid neuroevolutive algorithm is proposed, which combines Artificial Neuronal Networks such as Extreme Learning Machines, with evolutionary algorithms to optimize the trained models. The phenomenon under study occurs with a very low probability, for this reason the problem is so unbalanced, and it is necessary to resort to techniques focused on obtain good results by means of a reduction of the samples from the majority class, as the SMOTE approach. A feature selection is performed by the evolutionary algorithm in order to choose the best trained model. Finally, this model is evaluated by a test set and its accuracy performance is given. The accuracy obtained in the results is quite good in terms of classification performance.