Generative AI-Driven Frameworks for Intelligent Wind Energy Forecasting and Optimization

Generative AI-driven frameworks are transforming the forecasting and optimization of wind energy systems by overcoming the limitations of traditional statistical and deterministic models that struggle with the nonlinear and variable nature of wind resources. By leveraging advanced models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models, these frameworks facilitate the creation of synthetic data, enhance scenario simulation, and enable robust probabilistic forecasting, significantly reducing uncertainty and improving operational resilience. Integrating generative AI with optimization algorithms and digital twin architectures supports intelligent turbine placement, adaptive energy dispatch, and predictive maintenance, thereby increasing energy yield, reliability, and economic viability. This approach enables real-time decision support, rapid scenario analysis, and improved grid integration, accelerating the transition toward sustainable energy systems.