电子科技大学学报2025，Vol.54Issue(6)：916-923,8.DOI:10.12178/1001-0548.2024335
基于改进灰狼优化算法的TCN-BiGRU电力负荷预测
TCN-BiGRU power load prediction based on improved gray wolf optimization algorithm
摘要
Abstract
To improve the accuracy of short-term power load forecasting,this paper proposes a TCN-BiGRU model based on an improved grey wolf optimization algorithm.In this framework,the input sequence is first processed by an enhanced temporal convolutional network(TCN)to capture long-term dependencies,and then by an improved self-attention-optimized bidirectional gated recurrent unit(BiGRU)to extract bidirectional dependencies.an auto regression(AR)module and an election mechanism are integrated within the model to enhance forecasting accuracy.Finally,the model parameters of the TCN-BiGRU are optimized using the improved grey wolf optimization algorithm to further boost its overall performance.Experimental simulations demonstrate that the proposed model achieves a mean absolute percentage error(MAPE)of 4.974%,mean absolute error(MAE)of 0.029,and root mean square error(RMSE)of 0.034,outperforming mainstream benchmark models and effectively enhancing load forecasting accuracy.关键词
短期负荷预测/电力系统/灰狼优化算法/自注意力机制/组合模型Key words
short-term load forecasting/power system/wolf optimizer algorithm/self-attention mechanism/combinatorial model分类
计算机与自动化引用本文复制引用
刘杰,马子健,周博文,吴海滨..基于改进灰狼优化算法的TCN-BiGRU电力负荷预测[J].电子科技大学学报,2025,54(6):916-923,8.基金项目
黑龙江省自然科学基金(LH2023E086) （LH2023E086）
黑龙江省交通运输厅科技项目(HJK2024B002) （HJK2024B002）
