Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model

Haotian Guo, Keng-Weng Lao (), Junkun Hao and Xiaorui Hu
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Haotian Guo: State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, China
Keng-Weng Lao: State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, China
Junkun Hao: School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China
Xiaorui Hu: State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, China

Energies, 2025, vol. 18, issue 14, 1-24

Abstract: Driven by dual carbon targets, the scale of wind power integration has surged dramatically. However, its strong volatility causes insufficient short-term prediction accuracy, severely constraining grid security and economic dispatch. To address three key challenges in extracting temporal characteristics of strong volatility, adaptive fusion of multi-source features, and enhancing model interpretability, this paper proposes a Time-Domain Dual-Channel Adaptive Learning Model (TDDCALM). The model employs dual-channel feature decoupling: one Transformer encoder layer captures global dependencies while the raw state layer preserves local temporal features. After TCN-based feature compression, an adaptive weighted early fusion mechanism dynamically optimizes channel weights. The ACON adaptive activation function autonomously learns optimal activation patterns, with fused features visualized through visualization techniques. Validation on two wind farm datasets (A/B) demonstrates that the proposed method reduces RMSE by at least 8.89% compared to the best deep learning baseline, exhibits low sensitivity to time window sizes, and establishes a novel paradigm for forecasting highly volatile renewable energy power.

Keywords: short-term forecast of wind power; time-domain dual-channel adaptive learning model; ACON; adaptive weighted early fusion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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