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Support vector machines for wind speed prediction


  • Mohandes, M.A.
  • Halawani, T.O.
  • Rehman, S.
  • Hussain, Ahmed A.


This paper introduces support vector machines (SVM), the latest neural network algorithm, to wind speed prediction and compares their performance with the multilayer perceptron (MLP) neural networks. Mean daily wind speed data from Madina city, Saudi Arabia, is used for building and testing both models. Results indicate that SVM compare favorably with the MLP model based on the root mean square errors between the actual and the predicted data. These results are confirmed for a system with order 1 to system with order 11.

Suggested Citation

  • Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
  • Handle: RePEc:eee:renene:v:29:y:2004:i:6:p:939-947
    DOI: 10.1016/j.renene.2003.11.009

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    References listed on IDEAS

    1. Rehman, Shafiqur & Halawani, Talal Omar, 1994. "Statistical characteristics of wind in Saudi Arabia," Renewable Energy, Elsevier, vol. 4(8), pages 949-956.
    2. Mohandes, Mohamed A. & Rehman, Shafiqur & Halawani, Talal O., 1998. "A neural networks approach for wind speed prediction," Renewable Energy, Elsevier, vol. 13(3), pages 345-354.
    3. Mohandes, M. & Rehman, S. & Halawani, T.O., 1998. "Estimation of global solar radiation using artificial neural networks," Renewable Energy, Elsevier, vol. 14(1), pages 179-184.
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