A unified Bayesian filtering framework for multi-horizon wind speed prediction with improved accuracy. (November 2021)

• Record Type: Journal Article
• Authors: Cai, Haoshu; Jia, Xiaodong; Feng, Jianshe; Yang, Qibo; Li, Wenzhe; Li, Fei; Lee, Jay
• Is Part Of: Renewable energy. Volume 178(2021)
• Page Start: 709
• Page End: 719
• Publication Date: 2021-11
• Subjects: Wind speed prediction -- Bayesian filtering -- Unscented kalman filter -- Support vector machine -- Forecasting -- Gaussian process regression; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2021.06.092
• Languages: English
• ISSNs: 0960-1481

Abstract

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.