BWO-Optimized CNN-BiGRU-Attention Model for Short-Term Load Forecasting

Short-term load forecasting is essential for optimizing power system operations and supporting renewable energy integration. However, accurately capturing the complex nonlinear features in load data remains challenging. To improve forecasting accuracy, this paper proposes a hybrid CNN-BiGRU-Attention model optimized by the Beluga Whale Optimization (BWO) algorithm. The proposed method integrates deep learning with metaheuristic optimization in four steps: First, a Convolutional Neural Network (CNN) is used to extract spatial features from input data, including historical load and weather variables. Second, a Bidirectional Gated Recurrent Unit (BiGRU) network is employed to learn temporal dependencies from both forward and backward directions. Third, an Attention mechanism is introduced to focus on key features and reduce the influence of redundant information. Finally, the BWO algorithm is applied to automatically optimize the model’s hyperparameters, avoiding the problem of falling into local optima. Comparative experiments against five baseline models (BP, GRU, BiGRU, BiGRU-Attention, and CNN-BiGRU-Attention) demonstrate the effectiveness of the proposed model. The experimental results indicate that the optimized model achieves superior predictive performance with significantly reduced error rates in terms of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), along with a higher Coefficient of Determination (R2) compared to the benchmarks, confirming its high accuracy and reliability for power load forecasting.