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A neural networks approach for wind speed prediction

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  • Mohandes, Mohamed A.
  • Rehman, Shafiqur
  • Halawani, Talal O.

Abstract

This paper introduces neural networks technique for wind speed prediction and compares its performance with an autoregressive model. First, we studied the statistical characteristics of mean monthly and daily wind speed in Jeddah, Saudi Arabia. The autocorrelation coefficients are computed and the correlogram is found compatible with the real diurnal variation of mean wind speed. The stochastic time series analysis is found to be suitable for the description of autoregressive model that involves a time lag of one month for the mean monthly prediction and one day for the mean daily wind speed prediction. The results on a testing data indicate that the neural network approach outperforms the AR model as indicated by the prediction graph and by the root mean square errors.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:13:y:1998:i:3:p:345-354
    DOI: 10.1016/S0960-1481(98)00001-9
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    References listed on IDEAS

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    1. Rehman, Shafiqur & Halawani, Talal Omar, 1994. "Statistical characteristics of wind in Saudi Arabia," Renewable Energy, Elsevier, vol. 4(8), pages 949-956.
    2. Njau, Ernest C., 1994. "Predictability of wind speed patterns," Renewable Energy, Elsevier, vol. 4(2), pages 261-263.
    3. Njau, Ernest C., 1994. "An electronic system for predicting air temperature and wind speed patterns," Renewable Energy, Elsevier, vol. 4(7), pages 793-805.
    4. Njau, Ernest C., 1995. "Expressions for wind speed, relative humidity, rainfall, absolute humidity, vapour pressure and dew point as functions of temperature," Renewable Energy, Elsevier, vol. 6(1), pages 23-28.
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