Accurate forecasting of renewable energy is crucial for grid stability, efficient scheduling, and reliable power system operation. However, photovoltaic (PV) and wind systems are highly nonlinear and intermittent due to changing environmental conditions, making prediction challenging. This study proposes an optimization-driven framework for PV parameter estimation and wind power curve modeling using real-world datasets, including data from the 226.8 MW Anantapur Wind Farm and experimental PV measurements. It highlights that the effectiveness of metaheuristic algorithms is problem-specific, with performance varying based on model and data characteristics. A comparative analysis using metrics such as RMSE, convergence behavior, and reliability shows that Leader Harris Hawks Optimization performs best for PV parameter estimation, while Harris Hawks Optimization is superior for wind modeling. The framework improves prediction accuracy and provides a scalable solution for integrating renewable energy into modern power systems.
Vadla et al. (Wed,) studied this question.