In a groundbreaking study published in the journal *Sustainable Chemistry for a Sustainable Future*, researchers have uncovered the transformative potential of generative artificial intelligence (Gen-AI) in revolutionizing renewable energy forecasting and system optimization. Led by Erdiwansyah from the Centre for Automotive Engineering at Universiti Malaysia Pahang and the Department of Natural Resources and Environmental Management at Universitas Serambi Mekkah, the research highlights how advanced AI models are tackling longstanding challenges in renewable energy integration.
The study, which synthesizes findings from high-impact publications between 2023 and 2025, reveals that Gen-AI architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers are significantly enhancing the accuracy of solar and wind forecasting. “GAN-based models, in particular, have reduced the root mean square error (RMSE) in solar irradiance forecasting by 15–20%,” Erdiwansyah noted. This improvement is crucial for grid stability and energy optimization, as renewable energy sources are inherently variable and unpredictable.
The research also explores the application of Gen-AI in load prediction, energy storage management, and smart grid optimization. Time-series GAN-LSTM hybrids have shown remarkable accuracy in demand forecasting under nonlinear conditions, while VAE-driven dispatch models have achieved gains of 9–12% in energy efficiency and curtailment reduction. These advancements are not just academic; they have profound commercial implications for the energy sector. By improving forecasting accuracy and system efficiency, Gen-AI can reduce operational costs, enhance grid reliability, and accelerate the transition to renewable energy sources.
One of the most compelling aspects of this research is its exploration of Gen-AI’s integration with digital twins, federated learning, and AI–IoT frameworks. These technologies enable real-time, privacy-preserving optimization of complex energy systems, which is a game-changer for the industry. “The novelty of this review lies in mapping Gen-AI’s integration with these advanced frameworks,” Erdiwansyah explained. This integration allows for more dynamic and responsive energy management, which is essential for the future of smart grids.
However, the study also acknowledges challenges that need to be addressed for sustainable implementation. Issues such as model explainability, data privacy, and scalability are critical areas that require further research and development. As the energy sector continues to evolve, the insights from this study will be invaluable in shaping future developments. The research underscores the potential of Gen-AI to enhance system resilience, forecasting precision, and operational flexibility in renewable energy networks.
For the energy sector, the implications are clear: Gen-AI is not just a tool for the future; it is a necessity for the present. As renewable energy sources become increasingly integral to global energy systems, the ability to accurately forecast and optimize their performance will be crucial. This research provides a roadmap for leveraging Gen-AI to achieve these goals, paving the way for a more sustainable and efficient energy future.