# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 4 ML Models Classification for Wind Power
### ML Models Applied to Wind Power  
The specialized literature identifies many specific ML models. In what follows, ML models of wind power or wind energy are analyzed using the articles listed in WOS. The analysis covers the period between 1 January 2020 and 1 March 2025. The identified values are summarized in Figure 5.  
One of the most studied models used in this context is linear regression, which has been utilized in 149 scientific articles. This model is used to understand the relationships between different variables employed when analyzing the influence of the factors presented in the previous section on the performance of wind turbines, such as weather conditions.  
Figure 4: Co-occurrence map of terms for wind power approach using ML.  
The use of logistic regression in only 42 articles suggests that its application is more limited and valuable in classification problems, such as fault detection in wind turbines.  
Models such as KNNs, used in 63 articles, and SVMs, with 220 articles, have broad applicability in classifying and analyzing data from turbine monitoring systems. KNN is appreciated for its ability to work with small or medium datasets. SVM is preferred for complex datasets, especially when there is a clear separation line between the analyzed data classes. Although used in only 32 articles, decision trees represent a technique for interpreting and explaining the decisions made by models. They can be used for the optimization and control of wind turbines.  
On the other hand, with 224 articles, random forest represents an extension of decision trees and is often used in prediction and diagnostic applications due to its ability to handle large and complex datasets. ANNs and CNNs are ML models extensively studied in the specialized literature. With 478 articles for ANN and 340 for CNN, these models are extremely popular due to their ability to learn from complex data and identify hidden patterns in turbine operational data.  
Recurrent neural network (RNN) models have been identified in the literature, and 176 articles have been written about them. DL models have provided 867 articles in the literature. AdaBoost and gradient boosting models, with 17 and 114 articles, respectively, are used in classification and regression applications.  
Other techniques, such as K-Means (173 articles), density-based spatial clustering of applications with noise (DBSCAN) with 26 articles, PCA with 136 articles, and t-SNE (6 articles), are used in data analysis and dimensionality reduction. These are well suited for applications working with large wind turbine or wind farm datasets. Finally, Markov chains, with only 11 articles, are applied in modeling the probabilistic behavior of wind systems and can be used for forecasting wind variability and energy production.  
These results show that ML models have been intensively studied in the last five years. This analysis highlights the superiority of the ANN model in addressing ML models for the concept of wind energy.  
Figure 5: Overview of ML models used in wind power and wind energy research.