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

Weiqi Mao^1,2,3, Enbo Yu^1,*, Guoji Xu³, Xiaozhen Li³
1 Department of Bridge Engineering, Southwest Jiaotong University, Chengdu, 610031, China
2China Railway Construction Bridge Bureau Group Corporation, Wuhan, 430034, China
3 State Key Laboratory of Bridge Intelligent and Green Construction, Wuhan, 430034, China
* Corresponding Author: Enbo Yu. Email: email
(This article belongs to the Special Issue: Deep Learning for Energy Systems)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.069733

Received 29 June 2025; Accepted 01 September 2025; Published online 22 December 2025

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Abstract

Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration. This study presents a novel machine learning model that integrates clustering, deep learning, and transfer learning to mitigate accuracy degradation in 24-h forecasting. Initially, an optimized DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm clusters wind fields based on wind direction, probability density, and spectral features, enhancing physical interpretability and reducing training complexity. Subsequently, a ResNet (Residual Network) extracts multi-scale patterns from decomposed wind signals, while transfer learning adapts the backbone network across clusters, cutting training time by over 90%. Finally, a CBAM (Convolutional Block Attention Module) attention mechanism is employed to prioritize features for LSTM-based prediction. Tested on the 2015 Jena wind speed dataset, the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines. Key innovations include: (a) Physics-informed clustering for interpretable wind regime classification; (b) Transfer learning with deep feature extraction, preserving accuracy while minimizing training time; and (c) On the 2016 Jena wind speed dataset, the model achieves MAPE (Mean Absolute Percentage Error) values of 16.82% and 18.02% for the Weibull-shaped and Gaussian-shaped wind speed clusters, respectively, demonstrating the model’s robust generalization capacity. This framework offers an efficient and effective solution for long-term wind forecasting.