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A corrected hybrid approach for wind speed prediction in Hexi Corridor of China

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  • Guo, Zhenhai
  • Zhao, Jing
  • Zhang, Wenyu
  • Wang, Jianzhou

Abstract

Wind energy has been well recognized as a renewable resource in electricity generation, which is environmentally friendly, socially beneficial and economically competitive. For proper and efficient evaluation of wind energy, a hybrid Seasonal Auto-Regression Integrated Moving Average and Least Square Support Vector Machine (SARIMA–LSSVM) model is significantly developed to predict the mean monthly wind speed in Hexi Corridor. The design concept of combining the Seasonal Auto-Regression Integrated Moving Average (SARIMA) method with the Least Square Support Vector Machine (LSSVM) algorithm shows more powerful forecasting capacity for monthly wind speed prediction at wind parks, when compared with the single Auto-Regression Integrated Moving Average (ARIMA), SARIMA, LSSVM models and the hybrid Auto-Regression Integrated Moving Average and Support Vector Machine (ARIMA–SVM) model. To verify the developed approach, the monthly data from January 2001 to December 2006 in Mazong Mountain and Jiuquan are used for model construction and model testing. The simulation and hypothesis test results show that the developed method is simple and quite efficient.

Suggested Citation

  • Guo, Zhenhai & Zhao, Jing & Zhang, Wenyu & Wang, Jianzhou, 2011. "A corrected hybrid approach for wind speed prediction in Hexi Corridor of China," Energy, Elsevier, vol. 36(3), pages 1668-1679.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:3:p:1668-1679
    DOI: 10.1016/j.energy.2010.12.063
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    References listed on IDEAS

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    1. Palm, F. & Zellner, A., 1991. "To combine or not to combine? issues of combining forecasts," LIDAM Discussion Papers CORE 1991022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Yokuma, J. Thomas & Armstrong, J. Scott, 1995. "Beyond accuracy: Comparison of criteria used to select forecasting methods," International Journal of Forecasting, Elsevier, vol. 11(4), pages 591-597, December.
    3. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
    4. Winkler, Robert L., 1989. "Combining forecasts: A philosophical basis and some current issues," International Journal of Forecasting, Elsevier, vol. 5(4), pages 605-609.
    5. Makridakis, Spyros, 1989. "Why combining works?," International Journal of Forecasting, Elsevier, vol. 5(4), pages 601-603.
    6. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    7. Sfetsos, A., 2002. "A novel approach for the forecasting of mean hourly wind speed time series," Renewable Energy, Elsevier, vol. 27(2), pages 163-174.
    8. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    9. Sfetsos, A., 2000. "A comparison of various forecasting techniques applied to mean hourly wind speed time series," Renewable Energy, Elsevier, vol. 21(1), pages 23-35.
    Full references (including those not matched with items on IDEAS)

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