基于模态分解融合改进BiGRU的光伏功率预测OA

In order to meet the high precision requirement of PV power prediction in military energy scheduling,a dung beetle optimization(DBO)algorithm is proposed to integrate attention mechanism to optimize the bidirectional gated recurrent unit(BiGRU)photovoltaic power prediction method.Carry out analysis on influence factors and output characteristic of that photovoltaic power;carrying out multi-scale decomposition on the photovoltaic power data through complete empirical mode decomposition,and effectively separate high-frequency noise and low-frequency trend components;The multi-head self-attention(MHSA)mechanism is introduced to enhance the dynamic focusing ability of the model on key meteorological features,and the dung beetle optimization algorithm is combined to optimize the hyperparameters of the two-way gated recurrent unit network,which significantly improves the generalization performance of the model in complex military environments.The results show that the proposed model is significantly better than traditional comparison model in terms of MAE,RMSE and R²,and has a good prediction effect.