期刊
ENERGY CONVERSION AND MANAGEMENT
卷 126, 期 -, 页码 1084-1092出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2016.08.086
关键词
Wind speed prediction; Hybrid model; Gaussian process regression; Prediction interval
资金
- National Natural Science Foundation of China - China [61374006]
- Major Program of National Natural Science Foundation of China - China [11190015]
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. (C) 2016 Elsevier Ltd. All rights reserved.
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