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

Year 2025, Volume: 5 Issue: 1, 10 - 18, 28.02.2025

Necati Aksoy İstemihan Genç

https://doi.org/10.5152/tepes.2024.24027

Abstract

Renewable energy sources play an increasingly important role in meeting global energy demand while reducing carbon emissions and overall energy costs. Accurate forecasting of power generation in solar power plants is crucial for effective energy management and operational planning. This study proposes a novel hybrid prediction model that integrates several widely used machine learning algorithms to enhance the accuracy of solar power generation forecasting. Based on real meteorological and production data, the proposed hybrid model significantly outperforms individual prediction models. By incorporating meteorological parameters, the model provides more reliable short-term and long-term power predictions, thereby supporting improved decision-making processes in solar power plant operations. The results highlight the advantages of the proposed approach and offer valuable insights into improving the predictability and operational efficiency of solar power plants.

Keywords

Hybrid predictive model , machine learning , renewable energy ensemble learning , solar power prediction.

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