综合智慧能源2026，Vol.48Issue(1)：85-97,13.DOI:10.3969/j.issn.2097-0706.2026.01.009
基于评价因子重构与DECN-BiGRU的海岛微电网负荷预测
Load prediction for island microgrids based on evaluation factor reconstruction and DECN-BiGRU
摘要
Abstract
Aiming at the strong nonlinearity,non-stationarity,and multi-source coupling characteristics of island microgrid loads,a load prediction method was proposed,integrating robust empirical mode decomposition(REMD)based on evaluation factor reconstruction with detail-enhanced convolutional network(DECN)and bidirectional gated recurrent unit(BiGRU).Multi-scale feature decoupling was achieved through REMD and evaluation factor reconstruction.A DECN-BiGRU hybrid architecture was constructed to fuse local differences and global dependency features,and multi-task learning was introduced to optimize the coupling relationships among components.Experiments showed that the model reduced the mean absolute percentage error by 68.78%compared with traditional methods and reduced the mean absolute error by 68.97%compared with deep learning models,thereby verifying the effectiveness of multi-modal feature fusion and bidirectional modeling.The research findings provide reference for power scheduling and energy storage configuration in island microgrids.关键词
海岛微电网/负荷预测/鲁棒经验模态分解/细节增强卷积网络/双向门控循环单元/评价因子重构/多任务学习/储能Key words
island microgrid/load prediction/robust empirical mode decomposition/detail-enhanced convolutional network/bidirectional gated recurrent unit/evaluation factor reconstruction/multi-task learning/energy storage分类
能源科技引用本文复制引用
梁富光,马忠强..基于评价因子重构与DECN-BiGRU的海岛微电网负荷预测[J].综合智慧能源,2026,48(1):85-97,13.基金项目
国家电网公司科技项目(52139023000D)National Grid Company Science and Technology Projects(52139023000D) （52139023000D）
