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Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction

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Listed:
  • Salcedo-Sanz, Sancho
  • Ángel M. Pérez-Bellido,
  • Ortiz-García, Emilio G.
  • Portilla-Figueras, Antonio
  • Prieto, Luis
  • Paredes, Daniel

Abstract

This paper presents the hybridization of the fifth generation mesoscale model (MM5) with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at wind turbines in a wind park is an important parameter used to predict the total power production of the park. Our model for short-term wind speed forecast integrates a global numerical weather prediction model and observations at different heights (using atmospheric soundings) as initial and boundary conditions for the MM5 model. Then, the outputs of this model are processed using a neural network to obtain the wind speed forecast in specific points of the wind park. In the experiments carried out, we present some results of wind speed forecasting in a wind park located at the south-east of Spain. The results are encouraging, and show that our hybrid MM5-neural network approach is able to obtain good short-term predictions of wind speed at specific points.

Suggested Citation

  • Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    • Handle: RePEc:eee:renene:v:34:y:2009:i:6:p:1451-1457
    DOI: 10.1016/j.renene.2008.10.017
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    References listed on IDEAS

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