基于评价因子重构与DECN-BiGRU的海岛微电网负荷预测OA

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.