Machine learning applications in wind energy production: Current status,challenges,and future directions

Abstract

As wind energy capacity surpasses 1136 GW globally, ML technologies prove essential for achieving renewable energy targets and grid stability requirements. Machine learning (ML) has emerged as a transformative technology for wind energy systems, revolutionizing forecasting accuracy, operational efficiency, and system reliability. This comprehensive review synthesizes recent advances across 500+ peer-reviewed studies from 2020 to 2025, revealing 15–40% performance improvements over traditional methods across all major applications. Deep learning approaches achieve up to 99% accuracy in fault detection while optimized forecasting systems reduce Mean Absolute Percentage Error (MAPE) to 5–12% and demonstrate 20% increases in energy value through advanced prediction capabilities. The review examines ML applications spanning wind power forecasting, turbine control optimization, predictive maintenance, and emerging technologies including digital twins and physics-informed neural networks. Critical challenges including data availability, model interpretability, and cross-site generalization are addressed, while future research directions emphasize physics-informed ML, federated learning, and explainable AI approaches.

Graphical Abstract