Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal

Abstract: Renewable energy sources are increasingly critical in addressing global energy needs while reducing carbon emissions and energy costs. Accurate forecasting of power generation in solar power plants is essential for efficient energy management and planning. This study introduces a novel hybrid prediction model that combines several prevalent machine learning algorithms to improve the accuracy of solar power generation forecasting. Using real meteorological and production data, the proposed model significantly outperforms individual prediction models. The hybrid model's integration of meteorological data ensures more reliable short-term and long-term power predictions, contributing to improved decision-making in solar plant operations. The results demonstrate the advantages of this approach, providing valuable insights into enhancing the predictability and operational efficiency of solar power plants.Cite this article as: N. Aksoy and V. M. I. Genc, “Improving accuracy in solar power plant power generation prediction: A hybrid model proposal,” Turk J Electr Power Energy Syst., 2025; 5(1), 10-18.