The Impact of Generative AI in Renewable Energy Applications

With the global emphasis on both sustainable development and environmental protection, countries are more relying on renewable energy, such as wind and solar energy. Although carbon-free and environmentally friendly in terms of power generation process, the output of renewable energy systems is highly volatile and challenging to control because the operation and power generation of those systems depend greatly on environmental conditions. This paper presents a review of the latest advancement, challenges, and future directions driven by Artificial Intelligence (AI), with a special focus on the applications of generative AI in renewable energy. This study also introduces the GAIRE Framework (Generative Artificial Intelligence for Renewable Energy), a four-layer architecture integrating GenAI into energy infrastructures. GAIRE consists of the Data Layer for multimodal synthesis, the Model Layer for physics-informed generation, the Integration Layer linking digital twins and IoT systems, and the Governance Layer ensuring ethical and sustainable AI operation. A case study on wind turbine fault detection demonstrates GAIRE’s ability to combine generative learning with physical realism for predictive maintenance. The framework establishes a foundation for transparent, resilient, and net-zero-aligned renewable energy systems powered by generative intelligence.