Predicting global horizontal irradiance using regression-based machine learning models in high-altitude environments.

Global solar radiation is a fundamental component of the Earth's energy balance and plays a critical role in integrating renewable energy into electrical systems. However, its pronounced variability, particularly in high-altitude regions, constrains energy stability and complicates planning processes. This study evaluated regression-based and machine-learning models for 1-day-ahead prediction of global horizontal irradiance (GHI) in Juliaca, Peru (>3,800 m.a.s.l.), using 11 years (2010-2020) of daily NASA POWER data (n = 4,000 observations after cleaning; 80/20 chronological train/test split: training from 2010 to 2018 and testing from 2018 to 2020). During data auditing, we identified that the same-day clearness index (K T ) is algebraically related to the same-day GHI through the extraterrestrial radiation term, which would otherwise inflate the apparent predictive accuracy. Therefore, we restricted same-day predictors to cyclical solar geometry terms and used lagged specific humidity, clearness index, and irradiance values from the previous 1-3 days as legitimate autoregressive information. Under this leakage-free design, Linear Regression achieved the best performance (R 2 = 0.390, MAE = 0.677 kWh m-2d-1, and RMSE = 0.896 kWh m-2d-1), narrowly outperforming gradient boosting (R 2 = 0.384) and polynomial regression (R 2 = 0.381). Random Forest and shallow neural networks followed closely, with all six models exhibiting remarkably comparable performance. Feature-importance and SHAP analyses for the Random Forest model showed that previous-day irradiance and previous-day (lag-1) humidity were the dominant predictors. This pattern may be consistent with short-persistence cloud processes; however, cloud observations are required to verify this interpretation directly. The modest but realistic explanatory power obtained here, together with the diagnostic procedure used to achieve it, offers a template for detecting and avoiding under-reported leakage risk in solar radiation forecasting studies that use clearness-index-derived predictors.