Predicting Solar Photovoltaic Power Production Using Artificial Intelligence-Based Algorithms

Because data-driven AI-based methods can accommodate the intermittent nature of solar energy, they hold promise for forecasting solar Photovoltaic (PV) power generation. In order to determine which machine learning algorithm is the best effective in predicting the output of solar PV power, this study evaluates a number of well-constructed and optimized algorithms. In particular, the following four methods are examined: decision trees, random forests, extreme gradient boosting, and linear regression. These models are tested using the solar PV system that was put in place at the Applied Science Private University in Jordan. To compare the models equitably, three performance indicators are used: R-squared (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The most successful model is the random forest one, with an RMSE of 11.04 kW and an MAE of 7.7 kW, and with a 98% R2 value. The robust prediction outcomes imply that improving the random forest model might increase prediction accuracy even more.