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<p>The growing need to combat climate change has significantly increased the deployment of wind energy as a clean and sustainable power source. This paper presents a comprehensive review of recent developments in machine learning applications for wind energy systems, including turbine structural monitoring, fault diagnosis, predictive maintenance, and operational performance enhancement. Various machine learning approaches such as convolutional neural networks, support vector machines, ensemble learning techniques, deep neural networks, and physics-informed models are critically examined based on their effectiveness. The study emphasizes the importance of data preprocessing, feature engineering, and feature selection in improving predictive accuracy and model robustness. Emerging research directions, including Gaussian process regression, transfer learning strategies, and real-time fault detection systems, are also explored, highlighting their potential to enhance long-term forecasting reliability. The review aims to provide researchers and industry professionals with a clear understanding of current trends, challenges, and future prospects in AI-driven wind energy optimization. Beyond summarizing existing methodologies, the paper identifies key quantitative limitations within current research. Many forecasting models demonstrate improved performance metrics such as RMSE, MAPE, and R<sup>2</sup> however, these results are often dependent on site-specific datasets and show limited adaptability across varying geographical conditions and turbine configurations. Although deep learning and hybrid approaches achieve high accuracy, they frequently involve substantial computational requirements and limited interpretability, restricting their practical real-time application. Furthermore, predictive maintenance frameworks commonly face challenges related to inconsistent fault warning times and fluctuating false alarm rates, which can compromise operational decision-making. Unlike forecasting-centric reviews, this work uniquely integrates site suitability assessment with reliability applications across 102 studies, providing meta-analysed performance benchmarks and operational cost savings projections. By addressing these gaps, the review outlines critical areas for future research aimed at developing more robust, scalable, and practical machine learning solutions for wind energy systems.</p>

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A Comprehensive Review of Machine Learning Approaches for Wind Turbine Site Suitability, Prediction and Reliability Analysis

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Inam Ul Haq,
  • Abhishek Kumar

摘要

The growing need to combat climate change has significantly increased the deployment of wind energy as a clean and sustainable power source. This paper presents a comprehensive review of recent developments in machine learning applications for wind energy systems, including turbine structural monitoring, fault diagnosis, predictive maintenance, and operational performance enhancement. Various machine learning approaches such as convolutional neural networks, support vector machines, ensemble learning techniques, deep neural networks, and physics-informed models are critically examined based on their effectiveness. The study emphasizes the importance of data preprocessing, feature engineering, and feature selection in improving predictive accuracy and model robustness. Emerging research directions, including Gaussian process regression, transfer learning strategies, and real-time fault detection systems, are also explored, highlighting their potential to enhance long-term forecasting reliability. The review aims to provide researchers and industry professionals with a clear understanding of current trends, challenges, and future prospects in AI-driven wind energy optimization. Beyond summarizing existing methodologies, the paper identifies key quantitative limitations within current research. Many forecasting models demonstrate improved performance metrics such as RMSE, MAPE, and R2 however, these results are often dependent on site-specific datasets and show limited adaptability across varying geographical conditions and turbine configurations. Although deep learning and hybrid approaches achieve high accuracy, they frequently involve substantial computational requirements and limited interpretability, restricting their practical real-time application. Furthermore, predictive maintenance frameworks commonly face challenges related to inconsistent fault warning times and fluctuating false alarm rates, which can compromise operational decision-making. Unlike forecasting-centric reviews, this work uniquely integrates site suitability assessment with reliability applications across 102 studies, providing meta-analysed performance benchmarks and operational cost savings projections. By addressing these gaps, the review outlines critical areas for future research aimed at developing more robust, scalable, and practical machine learning solutions for wind energy systems.