Wind Power Prediction with Optimization and Clustering Techniques

In this study an approach for the prediction of wind power using nearest neighbour search and k-means clustering is studied and the results are compared to a prediction method based on artificial neural networks. The nearest neighbour search is combined with a population-based optimization algorithm for the selection of the input variables. This selection is done by a particle swarm optimization with two optimization approaches: the first one is a global selection for all weather situations and the second one uses the k-means clustering algorithm for a previous clustering of the weather data and builds specific local selections for each cluster. Almost three years of weather prediction data and measured wind power data from 10 wind farms in Germany are used in this study. We show that the presented method yields smaller prediction errors in comparison to the neural network based model getting manual selected input variables. The specific variable selection for the different clusters yields improved results compared to the global variable selection.