Enhancing the Efficacy of Short-Term Prediction Models for Solar Photovoltaic Systems: An Influence Examination of Chronological and Meteorological Factors

Abstract

The increasing energy demand, driven by population growth and technological advancements, necessitates a shift from traditional fossil fuel-based power generation to renewable energy systems. Solar photovoltaic panels offer a promising alternative; however, their energy output fluctuations present challenges in maintaining a reliable power source. Accurate estimation of solar energy generation is critical for enhancing the competitiveness of solar power plants and fostering social and economic progress. This paper aims to develop a machine learning model that utilizes meteorological and time-related data to predict solar photovoltaic power generation. It investigates how various factors, such as humidity and seasonal variations, impact prediction accuracy. Three different ML models were used and compared: Support vectore regression, XGBoost, and HistGradient Boost. To improve the performance of these models hyperparameter tuning techniques is used. The findings reveal that radiation, humidity levels, and seasonal variations are the most influential factors in predicting photovoltaic solar power generation. Additionally, incorporating seasonal and humidity data can enhance the machine-learning model’s accuracy by 17.9%. XGBoost model outperforms existing methods, achieving the highest accuracy of 95.76% when incorporating humidity and seasonal factors. Its lowest MAPE of 4.23 and highest <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 99.1. to assess model precision, an uncertainty analysis was performed using p-factor and q-factors. The results confirm model robustness and reliability in predictive performance.

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