Optimal Management of Solar Energy Generation Using Derivatives of Irradiation Angle Data with the Gradient Boosting Algorithm

Optimal management of solar energy generation is one of the key challenges in utilizing renewable energy resources, requiring the use of advanced methods and key variables. This paper examines the impact of solar irradiation angle data as one of the most important factors in predicting solar energy generation. Using the Gradient Boosting algorithm, a model is developed that can predict solar energy generation with high accuracy. In this study, data related to the solar irradiation angle under various environmental conditions were collected, and preprocessing steps such as cleaning, normalization, and feature selection were carried out. The prediction accuracy of the model was improved by adding the first and second derivatives of the irradiation angle as new features. The proposed model was evaluated using metrics such as mean absolute error, root mean squared error, and coefficient of determination, and the results showed excellent performance. The goal of this research is to present an intelligent system for optimizing solar energy generation in areas targeted for the construction of solar power plants. This approach can directly contribute to decision-making related to the development of solar power plants and improving energy efficiency.