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A hybrid technique for short-term wind speed prediction


  • Hu, Jianming
  • Wang, Jianzhou
  • Ma, Kailiang


This study proposes a hybrid forecasting approach that consists of the EWT (Empirical Wavelet Transform), CSA (Coupled Simulated Annealing) and LSSVM (Least Square Support Vector Machine) for enhancing the accuracy of short-term wind speed forecasting. The EWT is employed to extract true information from a short-term wind speed series, and the LSSVM, which optimizes the parameters using a CSA algorithm, is used as the predictor to provide the final forecast. Moreover, this study uses a rolling operation method in the prediction processes, including one-step and multi-step predictions, which can adaptively tune the parameters of the LSSVM to respond quickly to wind speed changes. The proposed hybrid model is demonstrated to forecast a mean half-hour wind speed series obtained from a windmill farm located in northwestern China. The simulation results suggest that the developed forecasting method yields better predictions compared with those of other popular models, which indicates that the hybrid method exhibits stronger forecasting ability.

Suggested Citation

  • Hu, Jianming & Wang, Jianzhou & Ma, Kailiang, 2015. "A hybrid technique for short-term wind speed prediction," Energy, Elsevier, vol. 81(C), pages 563-574.
  • Handle: RePEc:eee:energy:v:81:y:2015:i:c:p:563-574
    DOI: 10.1016/

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    References listed on IDEAS

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    Cited by:

    1. repec:eee:energy:v:129:y:2017:i:c:p:122-137 is not listed on IDEAS
    2. Yuyang Gao & Chao Qu & Kequan Zhang, 2016. "A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting," Energies, MDPI, Open Access Journal, vol. 9(10), pages 1-28, September.
    3. repec:eee:renene:v:114:y:2017:i:pb:p:670-685 is not listed on IDEAS
    4. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    5. Dong, Qingli & Sun, Yuhuan & Li, Peizhi, 2017. "A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China," Renewable Energy, Elsevier, vol. 102(PA), pages 241-257.
    6. Nantian Huang & Chong Yuan & Guowei Cai & Enkai Xing, 2016. "Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine," Energies, MDPI, Open Access Journal, vol. 9(12), pages 1-19, November.
    7. repec:eee:rensus:v:88:y:2018:i:c:p:297-325 is not listed on IDEAS
    8. repec:eee:appene:v:197:y:2017:i:c:p:151-162 is not listed on IDEAS
    9. Hur, J. & Baldick, R., 2016. "A new merit function to accommodate high wind power penetration of WGRs (wind generating resources)," Energy, Elsevier, vol. 108(C), pages 34-40.
    10. repec:eee:energy:v:125:y:2017:i:c:p:591-613 is not listed on IDEAS


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