Short-Term Photovoltaic Power Forecasting Based on EEMD Residual Secondary IWOA-VMD Decomposition and ISSA-Optimized BiGRU

With the global energy structure transitioning toward low-carbon and sustainable development, improving the stability and predictability of renewable energy generation has become a key challenge for achieving carbon neutrality goals. However, photovoltaic power output exhibits significant variability and uncertainty, and accurate power forecasting is of great significance for optimizing grid dispatch, improving renewable energy integration capacity, and reducing system reserve requirements. Therefore, this paper proposes a multi-stage prediction model that integrates Ensemble Empirical Mode Decomposition (EEMD), Improved Whale Optimization Algorithm-based Variational Mode Decomposition (IWOA-VMD), and an Improved Sparrow Search Algorithm (ISSA)-optimized Bidirectional Gated Recurrent Unit (BiGRU) network. Specifically, EEMD is first used to decompose the photovoltaic power sequence to extract Intrinsic Mode Functions (IMFs); then, the residual IMF is further decomposed using IWOA-optimized VMD to enhance low-frequency modeling capability; next, ISSA adaptively optimizes the hidden layer dimensions and learning rate of the BiGRU; Finally, each component is predicted individually, and the overall power sequence is reconstructed. Experimental results based on publicly available real photovoltaic data demonstrate that the proposed model outperforms BiGRU and several hybrid models in terms of MAE and RMSE. The research findings contribute to improving the accuracy of photovoltaic power forecasting, thereby providing technical support for the low-carbon transition and sustainable development of energy systems.