Optimizing solar power forecasting with metaheuristic algorithms and deep learning models for photovoltaic grid connected systems

As the integration of photovoltaic system into modern power grid continues to accelerate globally, accurate solar power forecasting becomes essential for optimizing energy dispatch, ensuring grid reliability, and sustaining large-scale renewable energy production. This study proposes a novel FHO-GRU-LSTM model, which sequentially combines Gated recurrent units (GRU) and Long short-term memory (LSTM) networks, with hyperparameters optimized through the Fire Hawk optimization (FHO) algorithm. This hybridization leverages the complementary learning strengths of GRU and LSTM while integrating a nature-inspired optimization strategy. The model is trained using time-based temporal indexing and employs a recursive forecasting strategy to effectively capture temporal dependencies.The proposed model is evaluated using two distinct photovoltaic technologies, Poly-crystalline (Array 1) and Mono-crystalline (Array 2) implemented within the PEARL system. Quantitative performance assessments based on standard error metrics and residual bias analysis reveal the superior accuracy and robustness of the FHO-optimized GRU-LSTM model. The model achieved R2 scores of 0.9964 and 0.9966 for Arrays 1 and Array 2, respectively, along with substantial reductions in root mean square error and mean absolute error at 12.67 and 23.40% for Array 1, and 23.29 and 24.52% for Array 2. These findings highlight the critical importance of advanced hyperparameter tuning in enhancing the generalization capability of deep learning models and reinforce their applicability in improving grid stability and promoting sustainable renewable energy integration.