Abstract:Due to the intermittency, instability, and randomness of solar energy, accurate short-term photovoltaic (PV) power forecasting presents significant challenges, hindering the integration of photovoltaic systems with smart grids. To address this, a method called WVPD (WOA-VMD and PSO-DSN) is proposed. First, variational mode decomposition (VMD) is applied to obtain multiple intrinsic mode functions (IMFs) components. Meanwhile, the whale optimization algorithm (WOA) is used to optimize the mode components and penalty factor parameters to resolve issues of VMD decomposition and mode mixing. Then, the spatial and temporal correlations of PV power and numerical weather prediction (NWP) data are utilized to construct a novel dual-stream network (DSN), which combines squeeze-and-excitation networks (SENet) and bidirectional gated recurrent units (BiGRU). In addition, particle swarm optimization (PSO) is used to optimize the learning rate and batch size in the DSN. Finally, experiments demonstrate that compared to deep learning hybrid models, the proposed WVPD method improves MSE by 78.6%, RMSE by 53.7%, and MAE by 37.7%, showing superior performance over state-of-the-art models. The code can be found at https://github.com/ruanyuyuan/PV-power-forecast.