A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes

Wind energy represents a solution for reducing environmental impact. For this reason, this research studies the elements that propose optimizing wind energy production through intelligent solutions. Although there are studies that address the optimization of turbine performance or other indirectly related factors in wind energy production, the optimization of wind energy production remains a topic insufficiently explored and synthesized in the literature. This research studies how machine learning (ML) techniques can be applied to optimize wind energy production. This research aims to study the systematic applications of ML to identify and analyze the key stages of optimized wind energy production. Through this research, case studies are highlighted by which ML methods are proposed that directly target the issue of optimizing the wind power process through wind turbines. From the total of 1049 articles obtained from the Web of Science database, the most studied ML models in the context of wind energy are the artificial neural networks, with 478 papers identified. Additionally, the literature identifies 224 articles that have studied random forest and 114 that have incorporated gradient boosting about wind power. Among these, 60 articles have specifically addressed the issue of optimizing wind energy production. This aspect allows for the identification of gaps in the literature. The research notes that previous studies have focused on wind forecasting, fault detection, or turbine efficiency. The existing literature addresses the indirect optimization of component performance. Thus, this paper identifies gaps in the current research, discusses ML algorithms in the context of optimizing wind energy production processes, and identifies future directions for increasing the efficiency of wind turbines through integrated predictive methods.