Solar-Irradiance Forecasting in Arid Climates: Benchmarking Machine-Learning Models and Probabilistic-Uncertainty Quantification Using Four Years of Riyadh Data

Precise short-horizon solar-irradiance forecasting is essential for grid-tied inverter control and battery scheduling in arid climates, yet model performance in regions with frequent dust remains under-characterized. This study benchmarks six machine-learning models—Linear Regression, Support Vector Regression (SVR), Random Forest, XGBoost, Gradient Boosting, and Long Short-Term Memory (LSTM)—for 1-, 3-, and 6-h-ahead Global Horizontal Irradiance forecasting using 34,871 hourly records from Riyadh, Saudi Arabia (2013–2016). All models were tuned with temporal cross-validation and evaluated on an independent 2016 test set. Two feature tracks were evaluated: a full twenty-five-feature set and a three-feature set (hour_sin, hour_cos, GHI_lag_1) selected by recursive feature elimination with 100% bootstrap stability. A four-feature SVR adding doy_cos is the empirical optimum at 1- and 3-h horizons, achieving an RMSE of 65.43 and 77.16 W/m2 (R2 0.956 and 0.948, skill scores 61.5% and 82.8%). At 6 h, Gradient Boosting with the full 25-feature set is best (RMSE 78.99 W/m2). The three-feature LSTM achieves 98.25, 148.84, and 233.47 W/m2. Split-conformal prediction achieves nominal 90% coverage (89.56%, 90.30%, 89.65%) with CRPS of 25.94, 31.36, and 32.42 W/m2, and intervals 7–21% narrower than quantile regression. A matched-condition dust analysis shows no statistically significant difference between dusty and clean days at 1- and 3-h horizons, while at 6 h, dusty days are significantly easier to forecast (37% lower RMSE, 95% CI [−37.46, −11.04]). A reserve allocation simulation using the four-feature SVR shows 9.1% savings at 1 h, 3.6% at 3 h, and 2.4% at 6 h at 94.9% empirical coverage. Rolling-origin validation across two temporal windows confirms stability (SVR standard deviation ≤ 1.8 W/m2).