Predicting Solar Photovoltaic Power Output in Saudi Arabia's Jazan Region: Performance Comparison of Machine Learning Models

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

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.

Links and sources

Comments