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Machine learning applications in wind energy production: Current status, challenges, and future directions
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DOI：10.1177/0309524X261423723.png)
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Abstract
En 中文
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.
Keywords:
wind energy
deep learning
wind power forecasting
condition monitoring
SCADA data analytics
physics-informed neural networks
predictive maintenance
reinforcement learning
systematic review
Journal
IF:
1.8
Papers:
105
Citations:
1.5K
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Cited Papers