Get access
Advertisement

Short-term wind speed prediction in wind farms based on banks of support vector machines

Authors

  • Emilio G. Ortiz-García,

    1. Grupo de Heurísticos Modernos de Optimización y Diseño de Redes (GHEODE), Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcala de Henares, Madrid, Spain
    Search for more papers by this author
  • Sancho Salcedo-Sanz,

    Corresponding author
    1. Grupo de Heurísticos Modernos de Optimización y Diseño de Redes (GHEODE), Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcala de Henares, Madrid, Spain
    • Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain
    Search for more papers by this author
  • Ángel M. Pérez-Bellido,

    1. Grupo de Heurísticos Modernos de Optimización y Diseño de Redes (GHEODE), Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcala de Henares, Madrid, Spain
    Search for more papers by this author
  • Jorge Gascón-Moreno,

    1. Grupo de Heurísticos Modernos de Optimización y Diseño de Redes (GHEODE), Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcala de Henares, Madrid, Spain
    Search for more papers by this author
  • Jose A. Portilla-Figueras,

    1. Grupo de Heurísticos Modernos de Optimización y Diseño de Redes (GHEODE), Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcala de Henares, Madrid, Spain
    Search for more papers by this author
  • Luis Prieto

    1. Iberdrola Renovables, Energy Resource Department, Via de los Poblados, 3 Edificio 9 28033 Madrid, Spain
    Search for more papers by this author

Abstract

Wind speed prediction is a key point in the management of wind farms because it is directly related to the power produced by each of a farm's turbines. Wind speed prediction is usually one of the most important tasks in wind farming, and companies that manage these farms invest large amounts of money to improve their prediction systems. In this paper, we propose an improvement to an existing wind speed prediction system, using banks of regression Support Vector Machines (SVMr) for a final regression step in the system. Several novel SVMr structures are proposed in this paper to manage the diversity in input data arising from the use of different global forecasting models and several parameterizations of a mesoscale model, included in the basic version of the prediction system. We show that the system implementing SVMr banks outperforms the basic system without taking into account diversity in the input data. It also performs better than a similar system using banks of multi-layer perceptrons. All the tests are carried out using real data from several wind turbines on a wind farm in southeast Spain. Copyright © 2010 John Wiley & Sons, Ltd.

Get access to the full text of this article

Ancillary