期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2018
卷号：186
期号：5
页码：012020
DOI：10.1088/1755-1315/186/5/012020
语种：English
出版社：IOP Publishing
摘要：We propose a long short-term memory (LSTM) network based encoder-decoder (E-D) model for wind power prediction (WPP). The LSTM-based E-D model is constructed as an auto-encoder for mapping the wind power (WP) time-series into a fixed-length representation, state of the trained E-D LSTM. Then, the representation concatenated with weather forecasting information is used as a new input to another multiple LSTM network to make WPP. Real data collected from a wind farm with capacity of 50 MW of Shan Xi province were used to verify the conclusions. Results illustrate that the proposed method improves the model generalization ability and lowers misspecification risk by utilizing the WP time relationship through auto-encoding (AE) process. Combining extracted representation with weather forecasting information further improves the prediction accuracy.
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