期刊论文

Maoyuan Zhang

英文

Applied Sciences-Basel

2076-3417

2023

13

6

4042-

10.3390/app13064042

Conceptualization, Methodology, Writing—original draft, P.X.; Supervision, Writing—review and editing, M.Z.; Supervision, Project administration, Z.C.; Validation, Visualization, B.W.; Funding acquisition, Formal analysis, C.C.; Software, Resources, R.L. All authors have read and agreed to the published version of the manuscript. This study is jointly supported by the Hubei Provincial Natural Science Foundation of China under grant number 2022CFD017, the Special Innovation and Development Program of the China Meteorological Administration under grant number CXFZ2023J044 and the Key Fund Project of Hubei Meteorological Bureau under grant number 2021Z08.

本校为其他机构

Due to the increasing proportion of wind power connected to the grid, day-ahead wind power prediction plays a more and more important role in the operation of the power system. This paper proposes a day-ahead wind power short-term prediction model based on deep learning (DWT_AE_BiLSTM). Firstly, discrete wavelet transform (DWT) is used to denoise the data, then an autoencoder (AE) technology is used to extract the data features, and finally, bidirectional long short-term memory (BiLSTM) is used for prediction. To verify the effectiveness of the proposed DWT_AE_BiLSTM model, we studied three di...