Using Machine Learning Algorithms to Forecast Solar Energy Power Output

Solar energy is an inherently variable energy resource, and the ensuing uncertainty in matching energy demand presents a challenge in its operational use as an alternative energy source. The factors influencing solar energy power generation include geographic location, solar radiation, weather conditions, and solar panel performance. Solar energy forecasting is performed using machine learning for better accuracy and performance. Due to the variability of solar energy, the forecasting window is an important aspect of solar energy forecasting that must be integrated into any machine learning model. This study evaluates the suitability of selected machine learning (ML) models comprising Linear Regression, Decision Tree, Random Forest and XGBoost, which have been proven to be effective at forecasting. The data forecasting horizon used was a 24-h window in steps of 30 min. We focused on the first 30-min, 3-h, 6-h, 12-h, and 24-h windows to gain an appreciation of the impact of forecasting duration on the accuracy of prediction using the selected machine learning algorithms. The study results show that Random Forest outperformed all other tested algorithms. It recorded the best values in all evaluation metrics: an average mean absolute error of 0.13, mean absolute percentage error of 0.6, root-mean-square error of 0.28 and R-squared value of 0.89.