Enhancing Wind Power Forecasting Accuracy Based on OPESC-Optimized CNN-BiLSTM-SA Model

Accurate wind power forecasting is critical for grid management and low-carbon transitions, yet challenges arise from wind dynamics’ nonlinearity and randomness. Existing methods face issues like suboptimal hyperparameters and a poor spatiotemporal feature integration. This study proposes OPESC-CNN-BiLSTM-SA, a hybrid model combining an optimized escape algorithm (OPESC), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) network, and self-attention (SA). The OPESC tunes critical hyperparameters, including the learning rate, the number of BiLSTM hidden units, self-attention key/query dimensions, and the L2 regularization strength, to enhance model generalization. Meanwhile, the CNN extracts spatial features, the BiLSTM captures bidirectional temporal dependencies, and SA dynamically weights critical features. Testing on real wind farm data shows the model reduces the RMSE by 30.07% and the MAE by 34.51%, and achieves an R2 of 97.06% compared to the baseline, demonstrating an improved accuracy for non-stationary energy time series forecasting.