Deep Learning Wind Power Prediction Model Based on Attention Mechanism-Based Convolutional Neural Network and Gated Recurrent Unit Neural Network

Accurate prediction of wind power is crucial for the efficient operation and risk management of wind farms. This paper introduces a deep learning model for wind power prediction that integrates an Attention mechanism with a convolutional neural network (CNN) and a gated recurrent unit (GRU) neural network. Addressing the randomness, intermittency, volatility and uncertainty of wind speed, we first apply swarm decomposition (SWD) to preprocess the original wind power data into subsequences. Subsequently, the CNN extracts spatial features, and the GRU identifies temporal correlations. The Attention mechanism enhances feature significance, further optimizing prediction accuracy. Complex error sequences generated by the CNN-GRU-Attention (CGA) model are corrected using the autoregressive integrated moving average (ARIMA). We evaluated the model's performance using three wind power datasets against 16 other models, employing six evaluation indices (MSE, RMSE, MAPE, Theil's U, TIC and SPL) and the Diebold-Mariano (DM) test and model confidence set (MCS) for model testing. Our results demonstrate the proposed model's superior accuracy and efficiency in predicting wind power.