Feature selection and hyperparameter tuning in transformer-based deep learning models for photovoltaic power forecasting using the Swordfish Movement Optimization Algorithm (SMOA)

Accurate photovoltaic (PV) power forecasting is essential for ensuring grid stability and efficient energy management in modern power systems. However, the nonlinear and intermittent nature of solar radiation limits the performance of traditional models. This study proposes a hybrid forecasting framework combining PatchTST with the Swordfish Movement Optimization Algorithm (SMOA) and its binary variant (bSMOA) to enhance prediction accuracy and model interpretability. The PatchTST captures complex temporal dependencies through self-attention and patch embedding, while bSMOA performs optimal feature selection and SMOA fine-tunes hyperparameters to improve convergence and generalization. Experimental results…

• PN
  Princess Nourah Bint Abdulrahman University