Short-Term Electrical Load Forecasting Based on IMBKA-BiGRU-Attention Model

Accurate short-term electrical load forecasting is of paramount importance for economic dispatch, reliable grid operation, and efficient demand-side management. However, hybrid forecasting frameworks constructed with deep learning models exhibit strong sensitivity to hyperparameter settings. Moreover, swarm-intelligence optimization algorithms are prone to premature convergence when tuning the hyperparameters of forecasting models, thereby degrading prediction performance. In addition, complex load sequences contain local fluctuations and key temporal segments that are difficult to capture using a single recurrent architecture. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a BiGRU-Attention network optimized by an improved multi-strategy black-winged kite algorithm (IMBKA). The BiGRU extracts bidirectional temporal dependencies from historical load windows, while the attention module assigns adaptive weights to informative time steps and suppresses redundant historical information. To improve hyperparameter optimization, IMBKA introduces Sobol sequence initialization and adaptive elite differential mutation. Sobol sequence initialization enhances population coverage, and adaptive elite differential mutation strengthens information exchange among high-quality individuals. Experimental results on electrical load datasets from Singapore, Australia, and Belgium show that IMBKA-BiGRU-Attention achieves favorable forecasting performance among the compared models. The proposed model obtains RMSE values of 70.07 MW, 159.49 MW, 231.82 MW, and 163.43 MW in the Singapore, Australian, Belgian weekday, and Belgian weekend experiments, respectively. Compared with the best-performing model among the evaluated baselines in each experiment, the RMSE is reduced by 4.65%, 16.48%, 3.34%, and 11.39%, respectively.