# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 6 Discussion
### Comparative Analysis of ML Models  
As specialists in the field of ML, the authors carried out the comparative analysis of the models by analyzing previous specialized literature. To conduct the comparative study, they classified ML models and assigned scores based on performance criteria. The previous presentation analyzed various ML models for wind energy applications. Table 2 summarizes key aspects of the most commonly used ML models, including their strengths, limitations, and typical applications in wind energy optimization, to provide a structured comparison of their effectiveness in different scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML Model** & **Strengths** & **Limitations** & **Applications in Wind Energy** \\ \hline Linear Regression & Simple, interpretable, fast & Limited accuracy for & Baseline forecasting, power \\  & to train & nonlinear relationships & curve modeling \\ Logistic Regression & Effective for classification & Not suitable for continuous & Fault detection, failure \\  & tasks & output prediction & classification \\ KNNs & Works well with small & Computationally & Wind pattern recognition, \\  & datasets, non-parametric & expensive for large & anomaly detection \\  & & datasets & \\ SVMs & High accuracy for & Slow training on large & Wind speed and power \\  & classification problems & datasets requires feature & prediction \\  & & scaling & \\ Decision Trees & Easy to interpret, handles & Prone to overfitting & Wind turbine performance \\  & nonlinear data & without pruning & assessment \\ Random Forest & Robust, reduces overfitting & Computationally & Energy output prediction, \\  & & expensive & predictive maintenance \\ Gradient Boosting & High accuracy, handles & Prone to overfitting with & Wind speed forecasting, \\  & complex patterns & small datasets & turbine fault prediction \\ XGBoost & Optimized for speed and & Requires careful & Wind farm energy production \\  & performance & hyperparameter tuning & modeling \\ ANNs & Handles complex nonlinear & Computationally intensive, & Power output optimization, \\  & relationships & requires large datasets & predictive maintenance \\ CNNs & Exels in pattern recognition & Requires large, labeled & Wind turbine image-based \\  & from spatial data & datasets & fault detection \\ RNNs & Effective for sequential and & Prone to vanishing & Wind speed and power \\  & time-series data & gradient problem & time-series forecasting \\ DL & High accuracy, learns & Requires high & Multi-variable wind energy \\  & complex patterns & computational power & forecasting \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of the identified ML models in the literature. Table 2 highlights the diversity of ML models applied in wind energy research. While ANN, random forest, and gradient boosting techniques consistently demonstrate high performance, simpler models like linear regression and decision trees are often used as benchmarks. Table 3 shows the comparison of ML models based on precision, efficiency, and weaknesses in optimizing wind energy production. The highest-precision models, such as ANNs, CNNs, RNNs, and gradient boosting models, provide the best forecasting and predictive maintenance precision. The scoring is performed on a scale from 1 to 5. For each criterion, the models received a score between 1 and 5, according to the following scheme:  
* 1 point: Impossible to use in practice and not used in optimizing wind energy production;
* 2 points: Low performance associated with simple models, with modest results in the field;
* 3 point: Average performance for which ML models are intensively analyzed in the literature but which have been identified with a series of limitations;
* 4 points: Good performance in the field of wind energy production optimization;
* 5 points: Excellent performance determined by complex models capable of efficiently optimizing energy production. The scores were determined through a comparative analysis of the reviewed studies.