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A hybrid statistical method to predict wind speed and wind power

Author

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  • Liu, Hui
  • Tian, Hong-Qi
  • Chen, Chao
  • Li, Yan-fei

Abstract

Accurate forecasting of wind speed and wind power is important for the safety of renewable energy utilization. Compared with physical methods, statistical methods are usually simpler and more suitable for small farms. Based on the methods of wavelet and classical time series analysis, a new short-term forecasting method is proposed. Simulation upon actual time data shows that: (1) the mean relative error in multi-step forecasting based on the proposed method is small, which is better than classical time series method and BP network method; (2) the proposed method is robust in dealing with jumping data; and (3) the proposed method is applicable to both wind speed and wind power forecasting.

Suggested Citation

  • Liu, Hui & Tian, Hong-Qi & Chen, Chao & Li, Yan-fei, 2010. "A hybrid statistical method to predict wind speed and wind power," Renewable Energy, Elsevier, vol. 35(8), pages 1857-1861.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:8:p:1857-1861
    DOI: 10.1016/j.renene.2009.12.011
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    References listed on IDEAS

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    5. Cadenas, Erasmo & Rivera, Wilfrido, 2007. "Wind speed forecasting in the South Coast of Oaxaca, México," Renewable Energy, Elsevier, vol. 32(12), pages 2116-2128.
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