A GAN-based approach to solar radiation prediction: data augmentation and model optimization for Saudi Arabia
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View abstract on PubMed
Summary
This summary is machine-generated.Generative adversarial networks (GANs) create synthetic solar radiation data to improve renewable energy predictions. This approach enhances model accuracy and adaptability, crucial for optimizing solar power systems.
Area Of Science
- Renewable Energy Systems
- Artificial Intelligence
- Data Science
Background
- Accurate solar radiation prediction is vital for renewable energy optimization but hindered by data scarcity and variability.
- Generative Adversarial Networks (GANs) are employed to generate high-quality synthetic solar radiation data, addressing data limitations.
Purpose Of The Study
- To develop a novel framework integrating GAN-generated synthetic data with machine learning and deep learning models.
- To improve the accuracy and adaptability of solar radiation prediction models across diverse climatic zones.
Main Methods
- A framework integrating GAN-generated synthetic data with CNN-LSTM architectures was developed.
- Models were trained and evaluated using augmented datasets, enhancing predictive accuracy and generalization.
Main Results
- Models trained on augmented datasets showed significant improvements: 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 for various climatic regions in Saudi Arabia.
Conclusions
- The proposed framework supports practical applications like photovoltaic system optimization and grid stability.
- This scalable and adaptable approach aligns with Saudi Arabia's Vision 2030 and global renewable energy goals.
- Further research into computational complexity and hyperparameter sensitivity is recommended for advancing sustainable energy solutions.
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