A CNN-GRU Deep Learning Prediction Model Based on Attention Mechanism

Le résumé fourni par la source

Accurate wind power forecasting is critical for efficient wind farm operation, revenue growth, and risk mitigation. This paper proposes a hybrid deep learning model integrating CNN, GRU, and an attention mechanism. To address wind speed volatility, VMD decomposes raw data into sub-sequences; CNN extracts spatial features, while GRU captures temporal patterns. The attention mechanism enhances key feature weights. For error refinement, ARIMA models residual sequences from the CNN-GRU-Attention (CGAM) framework. Validation on three datasets against ten models using six metrics confirms the model's superior accuracy and efficiency.

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