An intensive decomposition integration paradigm for short-term wind power forecasting based on feature extraction and optimal weighted combination strategy

被引：11

作者：

Wang, Jujie ^{[1
]}

Tang, Xudong ^{[1
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, Sch Management Sci & Engn, Nanjing 210044, Peoples R China

来源：

MEASUREMENT | 2023年 / 223卷

基金：

中国国家自然科学基金;

关键词：

Wind power forecasting; Feature extraction; Decomposed and ensemble method; Deep learning; EMPIRICAL MODE DECOMPOSITION; SPEED PREDICTION; WAVELET TRANSFORM; SEARCH ALGORITHM; MEMORY NETWORK; HYBRID MODEL; ELM; OPTIMIZATION; MULTISTEP; SELECTION;

D O I：

10.1016/j.measurement.2023.113811

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Wind power forecasting are crucial for enhancing power grid operation efficiency and ensuring the reliability and safety of the power supply. However, accurate wind power prediction has become a challenge due to the randomness and volatility of wind speed. In this study, an intensive decomposition integration paradigm based on feature extraction and optimal weighted combination strategy is proposed to make accurate predictions for wind power. The proposed method is verified by using two sets of wind power data from Inner Mongolia, China. Empirical results indicated that the proposed method has better prediction ability and stability than other comparative models.

引用

页数：13

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