A Hybrid XGBoost-Based Model Optimized with AVOA for Photovoltaic Power Forecasting

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Chinese explanation / 中文解读

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Original abstract

Accurate forecasting of photovoltaic (PV) power generation is of critical importance for grid stability, energy management, and production planning in renewable energy systems. However, the high variability of atmospheric conditions and sudden fluctuations in solar irradiance significantly limit the performance of conventional forecasting models. In this study, a hybrid photovoltaic power forecasting model based on the XGBoost algorithm, whose hyperparameters are optimized using the Artificial Vulture Optimization Algorithm (AVOA) and enhanced with the physics-based Radiation Stability and Efficiency Index (RSEI), designated as RSEI-XGBoost-AVOA, is proposed. The proposed approach provides a forecasting framework that is not only data-driven but also sensitive to physical processes by jointly modeling the temporal stability of solar irradiance and intraday generation dynamics. In this context, irradiance variability is represented through statistical measures, while intraday generation behavior is modeled using a sinusoidal efficiency function, and this structure is made more flexible through parameters optimized by AVOA. The model performance was evaluated separately for four different seasons using real data obtained from a 25 MW photovoltaic power plant in Türkiye. The results show that the proposed hybrid (RSEI-XGBoost-AVOA) model produces lower error values than both the standard XGBoost model and the AVOA-optimized model across all seasons. The Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values of the model were obtained as 4.15% and 0.4157 MW in summer, 6.07% and 0.2574 MW in winter, 8.03% and 0.7343 MW in spring, and 8.51% and 0.6135 MW in fall, respectively. These findings demonstrate that the proposed approach provides stable and generalizable forecasting performance even under highly variable atmospheric conditions and can be used as an effective decision-support tool for photovoltaic grid integration, short-term generation planning, and real-time energy management applications.

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