A Data-Driven Approach for Predicting Solar Energy Production Using Machine Learning Techniques

Machine learning techniques have a very wide application in various fields, including also renewable energy, which is now regarded as highly important topic due to its minimal negative impact on the environment. Solar energy is one of the most important renewable energy sources, and its installation capacity continues to grow. Forecasting solar energy production is a very highly interesting topic nowadays. In this paper, various machine learning algorithms are analysed, together with some hyperparameter optimization methods for the prediction of solar energy production. Different methods for forecasting solar energy production based on selected performance metrics are reviewed and analyzed. The dataset is built based on real data collected from a solar park in Kosova and the Meteorological Institute of Kosova, and the results have been derived using the Python programming language. From the results, we concluded that the Extreme Gradient Boosting algorithm, along with the Sequential Model-Based Optimization hyperparameter optimization method, achieves the best result, in terms of forecasting the energy production from the solar panels, with an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}$</tex> score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 5. 6 7 \%}$</tex>.