Gaussian Process Regression for numerical wind speed prediction enhancement. (February 2020)

• Record Type: Journal Article
• Authors: Cai, Haoshu; Jia, Xiaodong; Feng, Jianshe; Li, Wenzhe; Hsu, Yuan-Ming; Lee, Jay
• Is Part Of: Renewable energy. Volume 146(2020)
• Page Start: 2112
• Page End: 2123
• Publication Date: 2020-02
• Subjects: Wind speed prediction -- Multi-task Gaussian process -- Gaussian process regression -- Support vector machine -- Time series prediction -- Forecasting; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.08.018
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: This paper studies the application of Multi-Task Gaussian Process (MTGP) regression model to enhance the numerical predictions of wind speed. In the proposed method, a Support Vector Regressor (SVR) is first utilized to fuse the predictions from Numerical Weather Predictors (NWP). The purpose of this regressor is to map the numerical predictions at coarse geographical nodes to the desired turbine location. In subsequent analysis, this SVR prediction output is further enhanced by the MTGP regression model. Based on the validation results on the real-world data from large-scale off-shore wind farm, the prediction accuracies of the NWP are significantly improved at both the short-term horizons (1–6 h ahead) and the long-term horizons (7–24 h ahead) by employing the proposed method. More importantly, the short-term prediction accuracy after enhancement is found comparable or even better than the cutting-edge statistical models for short-term extrapolations. Highlights: A novel wind speed prediction method based on Gaussian Process is proposed. The method improves accuracy both in short-term and long-term predictions. The result is validated on two turbine's SCADA data from a real wind farm.