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

Authors:
Javier Lorenzo;Juan Méndez;Modesto Castrillón;Daniel Hernández
Affiliations:
Universidad de Las Palmas de Gran Canaria, Instituto Universitario SIANI, Las Palmas - Spain;Universidad de Las Palmas de Gran Canaria, Departamento de Informática y Sistemas, Las Palmas - Spain;Universidad de Las Palmas de Gran Canaria, Instituto Universitario SIANI, Las Palmas - Spain;Universidad de Las Palmas de Gran Canaria, Instituto Universitario SIANI, Las Palmas - Spain
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Year:
2011

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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 order to assess the accuracy of the proposed estimator, some experiments will be carried out with actual data of wind speed and power of an experimental 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.