Letters: Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks

Authors:
Sancho Salcedo-Sanz;Ángel M. Pérez-Bellido;Emilio G. Ortiz-García;Antonio Portilla-Figueras;Luis Prieto;Francisco Correoso
Affiliations:
Departamento de Teoría de la Señal y Comunicaciones, Universidad de Alcalá de Henares, Campus Universitario, 28871 Alcalá de Henares, Madrid, Spain;Departamento de Teoría de la Señal y Comunicaciones, Universidad de Alcalá de Henares, Campus Universitario, 28871 Alcalá de Henares, Madrid, Spain;Departamento de Teoría de la Señal y Comunicaciones, Universidad de Alcalá de Henares, Campus Universitario, 28871 Alcalá de Henares, Madrid, Spain;Departamento de Teoría de la Señal y Comunicaciones, Universidad de Alcalá de Henares, Campus Universitario, 28871 Alcalá de Henares, Madrid, Spain;Wind Resource Department, Iberdrola Renovables, Madrid, Spain;Department of Physics of the Earth, Astronomy and Astrophysics II, Universidad Complutense de Madrid, Spain
Venue:
Neurocomputing
Year:
2009

Citing 3
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Abstract

Wind speed prediction is a very important part of wind parks management. Currently, hybrid physical-statistical wind speed forecasting models are used to this end, some of them using neural networks as the final step to obtain accurate wind speed predictions. In this paper we propose a method to improve the performance of one of these hybrid systems, by exploiting diversity in the input data of the neural network part of the system. The diversity in the data is produced by the physical models of the system, applied with different parameterizations. Two structures of neural network banks are used to exploit the input data diversity. We will show that our method is able to improve the performance of the system, obtaining accurate wind speed predictions better than the one obtained by the system using single neural networks.