# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 3 Methodology
### Employed Methodology for ML Models Applied to Wind Power  
This research was conducted using the WOS database, which was chosen for its extensive high-impact coverage. Other databases, such as Scopus and IEEE Xplore, were considered but excluded due to content overlap and accessibility constraints. The search queries used in WOS covered various topics related to wind power and ML. The primary search string was the following:  
_TS = ((\"wind power\" OR \"wind energy\") AND \"machine learning\") AND PY = (2020-2025)_  
Figure 2 summarizes the entire research process presented in Section 4. This figure presents the evaluated ML algorithms from an initial WOS search, which is subsequently refined by applying the constraint \"optimization,\" followed by \"production,\" and then the algorithms' evaluation metrics. The flowchart diagram (Figure 2) illustrates the step-by-step methodology, outlining the search, selection, and analysis process. In Section 3.1, the bibliometric analysis included the following search:  
_TS = ((\"wind power\" OR \"wind energy\") AND \"ALGORITHM\") AND PY = (2020-2025)_  
The word ALGORITHM was replaced with various ML algorithms. The Supplementary Materials related to this research, ML_models.xlsx, presents in sheet 4.1 all the queries that enabled the analysis of the current state of the wind energy domain regarding ML for the interval 2020-2025. Subsequently, the search domain was narrowed by adding \"optimization\". In this way, it was intended to identify in the literature all articlesthat address the issue of optimizing processes involved in wind energy production while maintaining the previously analyzed context. Additionally, all queries are available in the Supplementary Materials ML_models.xlsx in sheet 4.2. The search follows the format:  
\[\textit{TS}=\textit{((\"wind power\" OR \"wind energy\") AND \"ALGORITHM\" AND \"optimization\")}\]  
\[\textit{AND PY}=\textit{(2020-2025)}\]  
In Section 4.3, the search was further narrowed by adding the word \"production\". In this way, the research was intended to study materials that address the theoretical optimization of wind energy production processes in practice. The narrowing of the domain aims to limit the focus to the practical application of theoretical concepts. The results are available in the Supplementary Materials ML_models.xlsx in sheet 4.3 in the general format:  
\[\textit{TS}=\textit{((\"wind power\" OR \"wind energy\") AND \"ALGORITHM\" AND \"optimization\" AND \"production\") AND PY = (2020-2025)}\]  
In Section 4.4, evaluation metrics for ML algorithms are introduced, and the queries are expanded to include all metrics associated with each algorithm individually. The general query is the following:  
\[\textit{TS}=\textit{((\"wind power\" OR \"wind energy\") AND \"ALGORITHM\" AND \"optimization\" AND \"MERTIC\") AND PY = (2020-2025)}\]  
The METRIC word was replaced systematically with the corresponding value for each evaluated algorithm. The results are summarized in Section 4.3, and each value correlated with the WOS query is detailed in the Supplementary Materials ML_models.xlsx, sheet 4.4.  
Figure 2: Step-by-step methodology of the ML models classification for wind power (Section 4 of the research).  
To refine the selection, the following inclusion criteria were applied:  
* Peer-reviewed journal articles and conference proceedings;
* Studies explicitly discussing ML models applied to wind energy optimization;
* Research addressing performance evaluation metrics for ML models.  
This analysis illustrates the current state of ML in wind energy optimization by refining search strategies, inclusion criteria, and bibliometric trends.