A Gaussian process regression based hybrid approach for short-term wind speed prediction

作者：Zhang, Chi; Wei, Haikun^*; Zhao, Xin; Liu, Tianhong; Zhang, Kanjian

来源：Energy Conversion and Management, 2016, 126: 1084-1092.

DOI：10.1016/j.enconman.2016.08.086

• Wind speed prediction
• Hybrid model
• Gaussian process regression
• Prediction interval

This paper proposes a hybrid model based on autoregressive (AR) model and Gaussian process regression (GPR) for probabilistic wind speed forecasting. In the proposed approach, the AR model is employed to capture the overall structure from wind speed series, and the GPR is adopted to extract the local structure. Additionally, automatic relevance determination (ARD) is used to take into account the relative importance of different inputs, and different types of covariance functions are combined to capture the characteristics of the data. The proposed hybrid model is compared with the persistence model, artificial neural network (ANN), and support vector machine (SVM) for one-step ahead forecasting, using wind speed data collected from three wind farms in China. The forecasting results indicate that the proposed method can not only improve point forecasts compared with other methods, but also generate satisfactory prediction intervals.

• 出版日期2016-10-15
• 单位东南大学