Predicting Solar Photovoltaic Power Output in Saudi Arabia's Jazan Region: Performance Comparison of Machine Learning Models
TL;DR
This research provides a direct comparison of five machine learning algorithms, that is, Decision Tree Regression (DTR), Multiple Linear Regression (MLR), Random Forest (RF), k‐Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost) for forecasting PV power output in Jazan, Saudi Arabia.
Abstract
Accurate prediction of solar panel energy output is vital for managing power systems effectively and maintaining a stable electrical grid. This is especially important in regions that rely heavily on renewable sources. This research provides a direct comparison of five machine learning (ML) algorithms, that is, Decision Tree Regression (DTR), Multiple Linear Regression (MLR), Random Forest (RF), k‐Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost) for forecasting PV power output in Jazan, Saudi Arabia. Jazan has a tropical desert climate with high humidity, seasonal wind speed variation, and coastal proximity, unlike the arid inland regions typically studied in the KSA. This makes it an ideal testbed for evaluating model robustness under varied meteorological parameters. The models were trained on a 6‐year (2017–2022) dataset comprising hourly measurements of four different meteorological parameters and evaluated using coefficient of determination ( R 2 ), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Wilcoxon signed‐rank test. Among the models used, XGBoost achieved the highest accuracy ( R 2 = 0.93, MAE = 7.3, RMSE = 20.38), outperforming all others. The results highlight the effectiveness of ensemble methods in PV power forecasting and offer valuable guidance for improving forecast accuracy in regions with comparable climates.