Data-Driven Prediction of Daily Global Solar Radiation in an Extreme Desert Climate: A Case Study of Kuwait

Estimating Global Solar Radiation (GSR) is crucial for various engineering applications, including agriculture, residential planning, and energy systems. Reliable ground-based GSR measurements are limited in desert climates due to sensor degradation, sparse coverage and harsh environmental conditions. This study evaluates four ensemble-based machine learning (ML) models—Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (G-Boost), and Extreme Gradient Boosting (XG-Boost)—alongside two deep learning (DL) models, Multi-Layer Perceptron (MLP) and hybrid Long Short-Term Memory–MLP (LSTM-MLP)—for predicting daily GSR in Kuwait, one of the world’s hottest regions. The models incorporate 13 daily meteorological and time-related input parameters, including temperature (average, maximum, and minimum), potential sunshine duration, cloud cover, humidity, wind speed, dew point temperature, sea-level pressure, and time-based factors (day of month, day of year, month, and year). Data were collected from a ground weather station between March 2015 and December 2020. Performance was assessed using five metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination (R2). Results showed strong predictive capabilities across all models, with G-Boost and XG-Boost demonstrating the best predictive performance (R2 = 0.996 and 0.994, respectively), while SVM achieved the highest held-out test R2 (0.9729) among the ensemble-based ML models. An additional hourly validation using the XG-Boost model demonstrated excellent agreement between observed and predicted hourly solar radiation, confirming that the proposed framework accurately captures short-term variability and is applicable for both daily solar resource assessment and short-term photovoltaic forecasting under extreme desert climate conditions. Among DL models, MLP outperformed LSTM-MLP. The models maintained consistently high accuracy across seasonal variations, including peak summer periods characterized by extremely high ambient temperatures. Feature importance analysis identified potential sunshine duration as the most influential factor, followed by maximum temperature. These findings demonstrate that the proposed framework provides accurate and reliable prediction of global solar radiation across multiple temporal resolutions, making it suitable for both long-term solar resource assessment and short-term photovoltaic forecasting in extreme desert climates.