A Spatially Validated AI Framework for Predicting Optimal Solar-PV Tilt Angles Across Egypt

Abstract

Selecting an appropriate photovoltaic (PV) tilt angle is essential for maximizing energy production from fixed-tilt systems. However, the conventional assumption that the optimum tilt equals the site latitude does not adequately represent the spatial and climatic variability of solar resources across Egypt. This study develops a physics-informed spatial machine-learning framework for predicting optimal fixed-PV tilt angles at annual, biannual, seasonal, and monthly time scales. A national ground-truth dataset was generated for 1,487 locations distributed over Egypt using a spatial grid. Hourly PVGIS-SARAH3 irradiance and meteorological data were processed for each site, and a physics-based PV engine evaluated 91 candidate tilt angles from  to  using Perez irradiance transposition. Geographic, irradiance, temperature, wind, and spatial-climatic features were then used to train and compare Linear Regression, Random Forest, Gradient Boosting, and HistGradientBoosting models against the conventional latitude rule. Model evaluation employed spatial block cross-validation and energy-based metrics, including Energy Regret. The final framework was further tested on 12 geographically unseen Egyptian sites. The physics-derived annual optimum ranged from  to , with a mean of . Linear Regression performed best for most prediction targets, while HistGradientBoosting provided improved performance for selected seasonal and monthly targets. External validation achieved a mean annual absolute error of , mean multi-target MAE of  , and mean RMSE of . Most importantly, the mean annual Energy Regret was only 0.00613%, remaining below 0.01%. A GUI deployment tool was also developed to provide location-specific tilt recommendations, energy estimates, schedules, and panel-orientation visualization. The results demonstrate that the proposed framework can support accurate and practical fixed-tilt PV deployment across Egypt.