A GAN-based approach to solar radiation prediction: data augmentation and model optimization for Saudi Arabia.

Background: Accurate solar radiation prediction is essential for optimizing renewable energy systems but remains challenging due to data scarcity and variability. This study addresses these challenges by employing generative adversarial networks (GANs) to generate high-quality synthetic solar radiation data. Methods: A novel framework was developed that integrates GAN-generated synthetic data with machine learning and deep learning models, including CNN-LSTM architectures. These models were trained and evaluated using augmented datasets to improve predictive accuracy and adaptability across diverse climatic zones. Results: Models trained on augmented datasets exhibited significant improvements, with root mean square error (RMSE) reduced by 15.2% and mean absolute error (MAE) decreased by 19.9%. The framework effectively bridged data gaps and enhanced model generalization, enabling applicability across various climatic regions in Saudi Arabia. Conclusions: The proposed framework facilitates practical applications such as photovoltaic system optimization, grid stability enhancement, and resource planning. By aligning with Saudi Arabia's Vision 2030 and global renewable energy objectives, this study presents a scalable and adaptable approach to advancing renewable energy systems. However, challenges such as computational complexity and hyperparameter sensitivity warrant further investigation, providing a robust pathway toward sustainable energy futures worldwide.