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Attention-Enhanced ResNet-LSTM Model with Wind-Regime Clustering for Wind Speed Forecasting

Weiqi Mao Enbo Yu Guoji Xu Xiaozhen Li

Computer Modeling in Engineering & Sciences2026，Vol.146Issue(1)：P.781-811,31.

Computer Modeling in Engineering & Sciences2026，Vol.146Issue(1)：P.781-811,31.DOI:10.32604/cmes.2025.069733

Attention-Enhanced ResNet-LSTM Model with Wind-Regime Clustering for Wind Speed Forecasting

Weiqi Mao ¹Enbo Yu ²Guoji Xu ³Xiaozhen Li³

作者信息

1. Department of Bridge Engineering,Southwest Jiaotong University,Chengdu,610031,China China Railway Construction Bridge Bureau Group Corporation,Wuhan,430034,China State Key Laboratory of Bridge Intelligent and Green Construction,Wuhan,430034,China
2. Department of Bridge Engineering,Southwest Jiaotong University,Chengdu,610031,China
3. State Key Laboratory of Bridge Intelligent and Green Construction,Wuhan,430034,China
折叠

摘要

关键词

Wind speed prediction/residual network/transfer learning/long short-term memory/attention mechanism

分类

信息技术与安全科学

引用本文复制引用

Weiqi Mao,Enbo Yu,Guoji Xu,Xiaozhen Li..Attention-Enhanced ResNet-LSTM Model with Wind-Regime Clustering for Wind Speed Forecasting[J].Computer Modeling in Engineering & Sciences,2026,146(1):P.781-811,31.

基金项目

funded by Science and Technology Research and Development Program Project of China Railway Group Limited(No.2023-Major-02) （No.2023-Major-02）

National Natural Science Foundation of China(Grant No.52378200) （Grant No.52378200）

Sichuan Science and Technology Program(Grant No.2024NSFSC0017). （Grant No.2024NSFSC0017）

Computer Modeling in Engineering & Sciences

ISSN：1526-1492

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