Sustainable PV Power Forecasting via MPA-VMD Optimized BiGRU with Attention Mechanism

Abstract

Accurate photovoltaic (PV) power generation forecasting is crucial for optimizing grid management and enhancing the reliability of sustainable energy systems. This study creates a novel hybrid model—MPA-VMD-BiGRU-MAM—designed to improve PV power forecasting accuracy through advanced decomposition and deep learning techniques. Initially, the Kendall correlation coefficient is applied to identify key influencing factors, ensuring robust feature selection for the model inputs. The Marine Predator Algorithm (MPA) optimizes the hyperparameters of Variational Mode Decomposition (VMD), effectively segmenting the PV power time series into informative sub-modes. These sub-modes are processed using a bidirectional gated recurrent unit (BiGRU) enhanced with a multi-head attention mechanism (MAM), enabling dynamic weight assignment and comprehensive feature extraction. Empirical evaluations on PV datasets from Alice Springs, Australia, and Belgium indicate that our hybrid model consistently surpasses baseline methods and achieves a 38.34% reduction in Mean Absolute Error (MAE), a 19.6% reduction in Root Mean Square Error (RMSE), a 4.41% improvement in goodness of fit, and a 33.91% increase in stability (STA) for the Australian dataset. For the Belgian dataset, the model attains a 96.32% reduction in MAE, a 95.84% decrease in RMSE, an 11.92% enhancement in goodness of fit, and an STA of 92.08%. We demonstrate the model’s effectiveness in capturing seasonal trends and addressing the inherent variability in PV power generation, offering a reliable solution to the challenges of instability, intermittency, and unpredictability in renewable energy sources.