Fairness-Controlled Metaheuristic Optimization of Random Forest for Solar Radiation Prediction Across Saudi Arabian Desert Climates

Accurate daily solar radiation estimates are needed to plan solar energy systems in arid regions, where long ground records are unavailable at most candidate sites. This study evaluates metaheuristic-optimized Random Forest models for daily global horizontal irradiance (GHI) at six climatically diverse Saudi sites (Riyadh, Tabuk, Najran, Dammam, Jeddah, and Turaif), using routinely available meteorological variables from the NASA POWER database for 2001–2024 (training 2001–2019, testing 2020–2024). Five metaheuristic optimization algorithms—the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), the Firefly Algorithm (FFA), Elephant Herding Optimization (EHO), and Grey Wolf Optimization (GWO)—were used to tune Random Forest hyperparameters under a fairness-controlled experimental design in which all algorithms received an identical number of fitness evaluations, search bounds and fitness function, with every configuration repeated over ten random seeds. Across the six sites, test MAPE ranged from 4.75% to 6.97% and R2 from 0.772 to 0.945, while the five algorithms differed by at most 0.023 percentage points of MAPE within any site. Neither a Friedman test across sites (MAPE: χ2 = 5.20, p = 0.267; R2: χ2 = 2.67, p = 0.615) nor within-site Kruskal–Wallis tests found a statistically significant difference among the algorithms. Bootstrap confidence intervals, leave-one-year-out validation and bias analysis by sky condition show that the remaining error is concentrated on cloudy and dust-affected days, and that Najran’s lower R2 reflects its small seasonal variance rather than larger errors. An untuned Random Forest performed almost identically to the tuned models. The choice of metaheuristic optimizer is therefore far less consequential for accuracy than site climate and input data quality.