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A unified Bayesian filtering framework for multi-horizon wind speed prediction with improved accuracy

Author

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  • Cai, Haoshu
  • Jia, Xiaodong
  • Feng, Jianshe
  • Yang, Qibo
  • Li, Wenzhe
  • Li, Fei
  • Lee, Jay

Abstract

This paper proposes a unified filtering framework for multi-horizon wind speed prediction. The novelty of this paper focuses on the integration of the short-term prediction model, the Numerical Weather Prediction (NWP) and a smoothing term into a unified framework based on Bayesian filters. In the proposed framework, the system state function of the Bayesian filter is constructed by a pre-trained static model based on Gaussian Process Regression (GPR) to enhance the short-term prediction accuracy. Meanwhile, NWP data is integrated by the system input of the state function of the Bayesian filter. The integration of NWP guarantees the medium/long-term prediction accuracy. The measurement function of the Bayesian filter is constructed as a smoothing term to further improve the overall accuracy of the proposed method. The prediction accuracy of the proposed filtering framework is extensively benchmarked with other existing approaches based on the data from an offshore wind farm. The benchmarking results suggest that the proposed method yields improved prediction performance in short-term horizon. For medium/long-term horizon, the best accuracy of RMSE is improved by about 46% compared with the benchmarks.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:709-719
    DOI: 10.1016/j.renene.2021.06.092
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    1. 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.
    2. Al-Yahyai, Sultan & Charabi, Yassine & Gastli, Adel, 2010. "Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3192-3198, December.
    3. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
    4. Hoolohan, Victoria & Tomlin, Alison S. & Cockerill, Timothy, 2018. "Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data," Renewable Energy, Elsevier, vol. 126(C), pages 1043-1054.
    5. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
    6. Zhao, Jing & Wang, Jianzhou & Guo, Zhenhai & Guo, Yanling & Lin, Wantao & Lin, Yihua, 2019. "Multi-step wind speed forecasting based on numerical simulations and an optimized stochastic ensemble method," Applied Energy, Elsevier, vol. 255(C).
    7. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Li, Wenzhe & Hsu, Yuan-Ming & Lee, Jay, 2020. "Gaussian Process Regression for numerical wind speed prediction enhancement," Renewable Energy, Elsevier, vol. 146(C), pages 2112-2123.
    8. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    9. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    10. 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.
    11. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    12. Wang, Han & Han, Shuang & Liu, Yongqian & Yan, Jie & Li, Li, 2019. "Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system," Applied Energy, Elsevier, vol. 237(C), pages 1-10.
    13. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    14. Zhang, Zhendong & Ye, Lei & Qin, Hui & Liu, Yongqi & Wang, Chao & Yu, Xiang & Yin, Xingli & Li, Jie, 2019. "Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression," Applied Energy, Elsevier, vol. 247(C), pages 270-284.
    15. Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
    16. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    17. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
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    Cited by:

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    2. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
    3. Yuxuan Shi & Yanyu Wang & Haoran Zheng, 2022. "Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network," Energies, MDPI, vol. 15(3), pages 1-18, January.
    4. Yukun Wang & Aiying Zhao & Xiaoxue Wei & Ranran Li, 2023. "A Novel Ensemble Model Based on an Advanced Optimization Algorithm for Wind Speed Forecasting," Energies, MDPI, vol. 16(14), pages 1-19, July.

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