# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 4 ML Models Classification for Wind Power
### Evaluation Metrics Employed by the ML Models for Optimized Wind Power Process  
ML models are evaluated by calculating performance metrics. In what follows, the number of articles for each model addressing specific aspects of performance metrics will be identified in the literature using WOS. Table 1 summarizes the results obtained from the searches provided by WOS. The queries to receive these results can be consulted in the Supplementary Materials ML_models.xlsx, sheet 4.4. Table 1 shows the scientific activity regarding algorithms for optimizing wind energy production according to the applied search criteria. The few articles identified in the WOS over the 2020-2025 period indicate difficulties in approaching this topic in a manner that meets all search criteria. Additionally, the scarcity of publications may reflect the emerging nature of this research field. Optimizing wind energy production through specific algorithms requires the development of complex models capable of integrating meteorological variables, historical data, and operational constraints. This entails significant investment in computational infrastructure and access to large databases. Another factor that could explain the low results is the difficulty of integrating optimization methods based on metrics in the wind energy domain. Most studies on wind energy focus on turbine efficiency, environmental impact, and grid integration. The use of optimization algorithms is a relatively new topic. Traditional algorithms, such as SVM, random forest, and ANN, do not appear to have been extensively used for this purpose, according to the data analyzed in Table 1.  
The results identified for other methods, such as ANN or gradient boosting, suggest the potential for using advanced ML techniques for wind energy optimization. However, these have not yet been widely explored. The lack of values for metrics such as accuracy, F1-Score, or AUC-ROC indicates that the models developed and presented in the literature are not sufficiently performant to yield conclusive results. Regarding unsupervised learning techniques, such as K-Means or DBSCAN, the zero values for evaluation metrics suggest that these methods have not been applied or have not succeeded in identifying patterns for optimizing wind energy production. This could be explained by the complexity of the wind energy production phenomenon, which depends on multiple variables that are difficult to capture through standard clustering methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ML Model** & **Performance Metrics** & **Number of Publications** \\ \hline \multirow{4}{*}{Linear Regression} & R\({}^{2}\) & 1 \\  & Mean Absolute Error & 3 \\  & Mean Squared Error & 2 \\  & Root Mean Squared Error & 2 \\ \hline \multirow{4}{*}{Logistic Regression} & Accuracy & 0 \\  & Precision & 0 \\  & Recall & 0 \\  & F\({}_{1}\)-Score & 0 \\  & AUC-ROC & 0 \\ \hline \multirow{4}{*}{KNNs} & Accuracy & 1 \\  & Precision & 1 \\  & Recall & 1 \\  & F\({}_{1}\)-Score & 1 \\  & AUC-ROC & 0 \\ \hline \multirow{4}{*}{SVMs} & Accuracy & 2 \\  & Precision & 0 \\  & Precision & 0 \\  & Recall & 0 \\  & F\({}_{1}\)-Score & 0 \\  & AUC-ROC & 0 \\ \hline \multirow{4}{*}{Random Forest} & Accuracy & 3 \\  & Precision & 2 \\  & F\({}_{1}\)-Score & 0 \\  & AUC-ROC & 0 \\ \hline \multirow{4}{*}{ANNs} & Accuracy & 6 \\  & Precision & 0 \\  & Recall & 0 \\  & F\({}_{1}\)-Score & 0 \\  & AUC-ROC & 0 \\ \hline \multirow{4}{*}{CNNs} & Accuracy & 1 \\  & Precision & 0 \\  & Recall & 0 \\  & F\({}_{1}\)-Score & 0 \\  & AUC-ROC & 0 \\ \hline \multirow{4}{*}{RNNs} & Accuracy & 3 \\  & Precision & 0 \\  & F\({}_{1}\)-Score & 0 \\  & AUC-ROC & 0 \\ \hline \multirow{4}{*}{Naive Bayes} & Accuracy & 0 \\  & Precision & 0 \\  & Recall & 0 \\  & F\({}_{1}\)-Score & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The number of articles in WOS addressing ML algorithms and performance metrics in wind energy optimization.