Research on a Short-Term Electric Load Forecasting Model Based on Improved BWO-Optimized Dilated BiGRU

Ziang Peng (), Haotong Han and Jun Ma
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Ziang Peng: Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125000, China
Haotong Han: Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125000, China
Jun Ma: Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125000, China

Sustainability, 2025, vol. 17, issue 21, 1-42

Abstract: In the context of global efforts toward energy conservation and emission reduction, accurate short-term electric load forecasting plays a crucial role in improving energy efficiency, enabling low-carbon dispatching, and supporting sustainable power system operations. To address the growing demand for accuracy and stability in this domain, this paper proposes a novel prediction model tailored for power systems. The proposed method combines Spearman correlation analysis with modal decomposition techniques to compress redundant features while preserving key information, resulting in more informative and cleaner input representations. In terms of model architecture, this study integrates Bidirectional Gated Recurrent Units (BiGRUs) with dilated convolution. This design improves the model’s capacity to capture long-range dependencies and complex relationships. For parameter optimization, an Improved Beluga Whale Optimization (IBWO) algorithm is introduced, incorporating dynamic population initialization, adaptive Lévy flight mechanisms, and refined convergence procedures to enhance search efficiency and robustness. Experiments on real-world datasets demonstrate that the proposed model achieves excellent forecasting performance (RMSE = 26.1706, MAE = 18.5462, R 2 = 0.9812), combining high predictive accuracy with strong generalization. These advancements contribute to more efficient energy scheduling and reduced environmental impact, making the model well-suited for intelligent and sustainable load forecasting applications in environmentally conscious power systems.

Keywords: load forecasting; deep learning; beluga whale optimization; dilated convolution; new type of feature engineering; energy efficiency (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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