Optimal forecasting of wind and solar PV power generation using enhanced HHO algorithm

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

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.

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