Machine learning and hybrid intelligence for wind energy optimization: A comprehensive state-of-the-art review

Wind energy plays a pivotal role in the global transition toward sustainable energy. However, its intermittent and stochastic nature presents challenges in achieving optimal performance, reliability, and seamless grid integration. Recent advances in machine intelligence—including machine learning (ML), deep learning (DL), and reinforcement learning (RL)—offer powerful tools to address these challenges across forecasting, control, maintenance, and diagnostics. This systematic review provides a comprehensive evaluation of how machine intelligence has contributed to the optimization of wind energy systems. These techniques have been applied to enhance turbine-level performance, reduce power losses, predict faults, and maximize energy yield under uncertain and dynamic conditions. Particular emphasis is placed on hybrid models that combine data-driven algorithms with physical dynamics and domain heuristics, enabling real-time, predictive, and autonomous wind farm operations. Furthermore, the study critically examines integration barriers such as noisy SCADA data, regulatory compliance, computational costs, and sustainability trade-offs. The findings highlight that multi-objective optimization—balancing energy production, system resilience, and cost efficiency—is central to the most successful implementations. Hybrid frameworks, explainable artificial intelligence (AI), edge computing, and transfer learning are identified as key enablers for scalable deployment. This review offers a comprehensive roadmap for the application of machine intelligence in advancing wind energy optimization and provides actionable insights for researchers, engineers, and policymakers committed to developing intelligent, adaptive, and sustainable wind power infrastructures.