# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 5 Analyzing Influencing Parameters in Wind Energy with ML
### Statistical Literature Considerations  
One parameter influencing the optimization of the wind energy production process is meteorological forecasting. The papers [92] and [93] published in 2024 and 2025 are identified in the specialized literature for analyzing meteorological forecasting within the context established by this analysis. Another parameter that influences wind energy production is wind speed itself. In this setup, 309 papers are identified in the specialized literature. Additionally, wind direction is another parameter that has generated 31 results in the specialized literature. Turbine design has produced a single paper in WOS search [94]. Climatic conditions are analyzed in the context of five papers. Grid connectivity has one \begin{table}
\begin{tabular}{c c c} \hline
**ML Model** & **Performance Metrics** & **Number of Publications** \\ \hline \multirow{4}{*}{AdaBoost} & Accuracy & 0 \\  & Precision & 3 \\  & Recall & 0 \\  & F\({}_{1}\)-Score & 0 \\ \hline \multirow{4}{*}{Gradient Boosting} & Accuracy & 3 \\  & Precision & 3 \\  & Recall & 1 \\  & F\({}_{1}\)-Score & 1 \\ \hline \multirow{4}{*}{XGBoos} & Accuracy & 1 \\  & Precision & 1 \\  & Recall & 0 \\  & F\({}_{1}\)-Score & 0 \\ \hline \multirow{4}{*}{K-Means} & Silhouette & 0 \\  & Davies–Bouldin Index & 0 \\  & Inertia & 0 \\ \hline \multirow{2}{*}{DBSCAN} & Silhouette & 0 \\  & Davies–Bouldin Index & 0 \\ \hline \multirow{2}{*}{PCA} & Explained Variance Ratio & 0 \\  & Reconstruction Error & 0 \\ \hline \multirow{2}{*}{t-SNE} & Perplexity & 0 \\  & Trustworthiness & 0 \\ \hline \multirow{2}{*}{Markov Chains} & Transition Matrix & 0 \\  & Stationary Distribution & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._associated paper in the literature [95]. Elements such as terrain topography or turbine height have not generated results in the literature. These values are synthesized in Figure 8, which presents the number of papers for each identified parameter. The first identified parameter that influences wind energy production is wind speed itself. In this setup, 309 papers are identified in the specialized literature. Additionally, wind direction is another parameter that has generated 31 results in the specialized literature. Turbine design has produced a single paper in WOS search [94]. Climatic conditions are analyzed in the context of five papers. Grid connectivity has one associated paper in the literature [95]. Elements such as terrain topography or turbine height have not generated results. These values are synthesized in Figure 8, which presents the number of papers for each identified parameter. Three hundred and two articles address wind turbines from an ML perspective. Of these, 60 contain optimization elements for the wind energy production process. In the 60 publications, 317 keywords were identified. The frequency distribution of keywords across the 60 publications is illustrated in Figure 9. In total, 194 distinct keywords were identified. Notably, the term \"optimization\" appeared in 17 of the 60 publications. The analysis in Figure 9 provides insights into research trends in wind energy and optimization through ML. The identification of 317 keywords underscores the diversity of approaches and topics explored. The fragmentation of research interests is evident, as 194 keywords were mentioned only once. Out of these 60 articles, the two published in 2025 were excluded, as the analysis results would not have been relevant in this context. Subsequently, the number of articles published each year was counted. The graph in Figure 10 shows the growing interest in optimizing the wind energy process using ML techniques. Of the 60 papers discussed above, 2 consider failure detection [96; 97] as an optimization wind power objective. The literature addresses optimizing wind energy production using ML in a single paper [98].