Prediction of renewable solar energy production from meteorological data using machine learning methods

Solar energy power plants play a significant role in meeting the demand for clean and sustainable energy. However, variable weather conditions, seasonal effects, and similar factors can result in the need for energy overproduction to be stored or lead to costs associated with energy deficiency. These situations can result in inefficiencies in solar energy production. The integration of solar energy data with meteorological data, using machine learning techniques, provides a valuable resource for predicting solar energy production, enhancing system efficiency, and developing more sustainable energy strategies. The primary objective of this study is to enhance efficiency in the energy sector and optimize energy production by using machine learning techniques, including Random Forest, Decision Trees, K-Nearest Neighbors, Multiple Linear Regression, Ridge Regression, Lasso Regression, and Linear Regression, with energy generated from solar panels and meteorological data. Additionally, error performance analyses, such as R-squared, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error, have been employed to assess the outcomes of the applied machine learning methods. This study aims to assess the results achieved by existing models and compare their successes. According to the research findings, the Random Forest Algorithm stands out as the most successful algorithm, achieving the highest R-squared score of 0.968420, the lowest Mean Squared Error value of 0.000764, and the lowest Mean Absolute Error value of 0.012567. The Decision Trees algorithm ranks second in performance. The other algorithms, in order of success, are Multiple Linear Regression, K-Nearest Neighbors, Ridge Regression, Lasso Regression, and Linear Regression. These results accurately reflect the fundamental findings of the study. Keywords: Solar Energy, Solar Power Plant, Machine Learning, Machine Learning Algorithms, Meteorology, Python, Random Forest Algorithm, Decision Trees Algorithm, Multiple Linear Regression, K-Nearest Neighbors, Ridge Regression, Lasso Regression, Linear Regression