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A combined filtering strategy for short term and long term wind speed prediction with improved accuracy

Author

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  • Cai, Haoshu
  • Jia, Xiaodong
  • Feng, Jianshe
  • Yang, Qibo
  • Hsu, Yuan-Ming
  • Chen, Yudi
  • Lee, Jay

Abstract

Wind Speed (WS) prediction plays a more and more important role in the wind farm operation and maintenance. In current literature, the short term (<6 h ahead) and medium/long (>6 h ahead) WS prediction are normally provided by different models. The statistical models are found effective in short-term prediction while the Numerical Weather Prediction (NWP) model is important to ensure the medium/long-term prediction accuracy. Driven by the needs of enhanced predictor that is effective for multiple time scales, this paper proposes a novel filtering strategy which integrates the statistical predictors and the NWP model outputs into one unified framework. Based on the proposed filtering strategy, a combined predictor SVR + SDA + UKF (Support Vector Regression + Stacked De-noising Auto-encoder + Unscented Kalman Filter) is proposed and validated. In the proposed predictor, the SVR term propagates the state vector of UKF and ensures short-term prediction accuracy. The SDA term fuses the NWP model outputs and mainly contributes to medium/long-term prediction accuracy. Consequently, the proposed method achieves improved accuracy in both short and medium/long-term WS prediction. In the case studies, the effectiveness of the proposed filtering strategy and the superiority of the predictor are demonstrated by the real-world data collected from an off-shore wind farm.

Suggested Citation

  • Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Yang, Qibo & Hsu, Yuan-Ming & Chen, Yudi & Lee, Jay, 2019. "A combined filtering strategy for short term and long term wind speed prediction with improved accuracy," Renewable Energy, Elsevier, vol. 136(C), pages 1082-1090.
    • Handle: RePEc:eee:renene:v:136:y:2019:i:c:p:1082-1090
    DOI: 10.1016/j.renene.2018.09.080
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    References listed on IDEAS

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    1. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
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    Cited by:

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    6. Huang, Yu & Zhang, Bingzhe & Pang, Huizhen & Wang, Biao & Lee, Kwang Y. & Xie, Jiale & Jin, Yupeng, 2022. "Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion," Renewable Energy, Elsevier, vol. 192(C), pages 526-536.
    7. Neeraj Bokde & Andrés Feijóo & Nadhir Al-Ansari & Siyu Tao & Zaher Mundher Yaseen, 2020. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling," Energies, MDPI, vol. 13(7), pages 1-23, April.
    8. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Yang, Qibo & Li, Wenzhe & Li, Fei & Lee, Jay, 2021. "A unified Bayesian filtering framework for multi-horizon wind speed prediction with improved accuracy," Renewable Energy, Elsevier, vol. 178(C), pages 709-719.
    9. Lu, Peng & Ye, Lin & Pei, Ming & Zhao, Yongning & Dai, Binhua & Li, Zhuo, 2022. "Short-term wind power forecasting based on meteorological feature extraction and optimization strategy," Renewable Energy, Elsevier, vol. 184(C), pages 642-661.
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