Advanced Intelligent Approach for Solar PV Power Forecasting Using Meteorological Parameters for Qassim Region, Saudi Arabia

Solar photovoltaic (SPV) power penetration in dispersed generation systems is constantly rising. Due to the elevated SPV causing a lot of problems system stability, sustainability, reliable electricity production, and quality, it critical forecast using climatic parameters. The suggested model built with meteorological conditions as input parameters, effects such variables on predicted have been studied. primary goal this study examine effectiveness optimization-based forecasting models based novel salp swarm algorithm due its excellent ability for exploration exploitation. To power, recently designed approach that (SSA) used. performance optimization estimated terms statistical parameters which include Root Mean Square Error (RMSE), (MSE), Training Time (TT). test reliability validity, proposed compared grey wolf (GWO) Levenberg–Marquardt-based artificial neural network algorithm. values RMSE MSE obtained SSA come out 1.45% 2.12% are lesser when other algorithms. Likewise, TT 12.46 s less than GWO by 8.15 s. outperforms intelligent techniques robustness. method applicable load management operations microgrid environment. Moreover, may serve road map Saudi government’s Vision 2030.