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

ABSTRACT:

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 (

SUBMITTER: Alameen A 

PROVIDER: S-EPMC12453800 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature