Short-term wind power forecast based on cluster analysis and artificial neural networks

Abstract

In this paper an architecture for an estimator of short-term wind farm power is proposed. The estimator is made up of a Linear Machine classifier and a set of k Multilayer Perceptrons, training each one for a specific subspace of the input space. The splitting of the input dataset into the k clusters is done using a k-means technique, obtaining the equivalent Linear Machine classifier from the cluster centroids. In or- der to assess the accuracy of the proposed estimator, some experiments will be carried out with actual data of wind speed and power of an exper- imental wind farm. We also compute the output of an ideal wind turbine to enrich the dataset and estimate the performance of the estimator on one isolated turbine.