Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/17887
Title: Short-term wind power forecast based on cluster analysis and artificial neural networks
Authors: Lorenzo Navarro, José Javier 
Méndez Rodríguez, Juan Ángel 
Castrillón-Santana, Modesto 
Hernández Sosa, José Daniel 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Prediction
Issue Date: 2011
Project: Tecnicas de Visión Para la Interacción en Entornos de Interior Con Elaboración Mapas Cognitivos en Sistemas Perceptuales Heterogéneos. 
Journal: Lecture Notes in Computer Science 
Conference: 11th International Work-Conference on Artificial Neural Networks (IWANN) 
11th International Work-Conference on on Artificial Neural Networks, IWANN 2011 
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.
URI: http://hdl.handle.net/10553/17887
ISBN: 978-3-642-21501-8
9783642215001
ISSN: 0302-9743
DOI: 10.1007/978-3-642-21501-8_24
Source: Cabestany J., Rojas I., Joya G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6691. Springer, Berlin, Heidelberg
Appears in Collections:Actas de congresos
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