# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 6 Discussion
### Detailed Analysis of Results  
In this paper, a bibliometric analysis was conducted to evaluate the application of ML models in wind energy optimization. The study selection was based on a search in the WOS database. The primary search queries used in WOS included general ML models in wind energy. Next, optimization-specific studies were extracted, and production-related studies refined the result. Lastly, performance evaluation metrics related to ML, production, and optimization were analyzed. The search covered studies published between 1 January 2020 and 1 March 2025, to ensure that only the recent research was included. Among the analyzed models, ANN provided the most articles (478), followed by random forest (224) and gradient boosting (114). These results reflect the ability of these models to handle large volumes of data and model complex relationships between wind energy production parameters.  
Scientific papers addressing wind energy production optimization without ML techniques were identified in the following analysis scenario to enable a comparative analysis with ML-based results. The results indicated naive Bayes had the highest number of articles (265), followed by ANN (174) and CNN (83).  
In the third scenario, ML models were introduced alongside optimization problems, with 27 articles identified for CNN, 12 for SVN, and 10 for random forest and K-means. These results are explained by CNNs used for short-term wind energy production forecasting, K-Means for energy output prediction, and the other models for meteorological predictions.  
The research has continued by investigating ML model performance evaluation metrics using standard tools such as accuracy, precision, recall, etc. All 17 identified models were quantified through these metrics. The most consistent results were obtained for linear regression, evaluated via R\({}^{2}\) in one article, mean absolute error in three articles, and mean squared error and root mean squared error in two articles.  
These results demonstrate that models are not consistently evaluated in the literature when addressing wind energy production optimization using ML. Future research should address this gap.  
After analyzing the four scenarios, factors influencing the optimization process were examined. The literature identified 1049 articles addressing ML perspectives, with narrowed searches yielding 309 for wind speed, 31 for wind direction, 5 for climatic conditions, 2 for meteorological forecasting, 1 for grid connectivity, and 1 for turbine design.  
Subsequently, wind turbines were analyzed as the central element of optimization, with 302 articles identified. Of these, 60 included elements specific to the wind energy production process. For the 60 publications, 317 keywords were identified, 194 of which were unique (not shared between articles). Among these, 17 publications included the term \"optimization\". These findings highlight the fragmented approach to optimization in this field, suggesting the need for unified frameworks in future studies.  
To assess researchers' interest in optimizing wind energy production using ML, a statistical analysis revealed growth from two articles in 2020 to eighteen in 2024. The year 2025 was excluded due to incomplete monthly data.  
Regarding publications disseminating the most research on ML-based optimization, MDPI led with 17 articles, followed by PERGAMON-Elsevier (12) and Elsevier (10). This analysis aimed to highlight outlets with heightened interest in the topic.  
Finally, the 60 reference articles were categorized by influencing parameters and subcategorized into review, research, or comparative analysis articles. Only 11 review articles were identified across five relevant categories.