# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 7 Conclusions  
This research studied 17 ML models for optimizing wind energy production. The analysis of the papers that identify the influencing parameters in the process is presented for detailed discussion. Among the main findings are the following:  
* Frequent use of advanced ML models, such as ANNs, random forest, and gradient boosting, due to their ability to analyze complex data;
* Traditional models, such as linear and logistic regression, are used less frequently, as they cannot capture the complexity of phenomena influencing wind energy production;
* Unsupervised models, such as K-means and DBSCAN, are not significantly utilized, suggesting that cluster analysis is not yet widely exploited in this field;
* Growing interest in hybrid methods, which combine traditional and advanced approaches to improve predictions;
* Current limitations include difficulty accessing relevant data and the lack of standardized methodologies for evaluating model performance specific to wind energy production. This research demonstrated that using ML models for optimizing wind energy production is not only feasible but also necessary to increase the efficiency of this sector. The literature analysis confirmed the hypothesis that ML models can improve the performance of wind turbines through production forecasting and optimization of influencing factors. The results emphasize that advanced ML methods enable better modeling of wind energy variability, leading to more efficient production planning and reduced operational costs. By analyzing the data and models studied, the following general conclusions can be drawn:  
* Neural network-based models are the most efficient for forecasting wind energy production due to their ability to analyze complex patterns in historical data;
* Random forest and gradient boosting models help identify and optimize factors influencing turbine performance;
* Hybrid models, which combine ML techniques, demonstrate the potential of integrating multiple methods into a unified approach;
* Integrating ML into wind farm management reduces uncertainty related to wind variability and improves resource utilization. This study identified correlations between various variables involved in wind energy production. The most significant highlighted relationships including the following:  
* Wind speed and energy production;
* Turbine layout;
* Predictive maintenance and turbine durability. The relevance and impact of the research on the wind energy field have implications for both the scientific community and the energy industry. The main contributions are the following:  
* Improving wind energy forecasting strategies;
* Increasing the operational efficiency of wind turbines;
* Reducing costs and environmental impact;
* Opening new research directions. Based on the results and identified limitations, the following directions are recommended for future research:  
* Developing more extensive and accurate datasets;
* Exploring more advanced ML methods;
* Optimizing hybrid architectures;* Developing standardized methodologies for evaluating the performance of models explored for optimizing wind energy production processes. This research demonstrates that using ML models represents a strategy for optimizing wind energy production. Advanced models provide enhanced data analysis capabilities and can improve the operational efficiency of wind turbines. However, the application of these technologies is still limited by data access, methodology standardization, and hybrid model integration challenges. Therefore, the future research should focus on overcoming these obstacles to maximize the positive impact of ML on the renewable energy industry. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15073758/sl](https://www.mdpi.com/article/10.3390/app15073758/sl)]([https://www.mdpi.com/article/10.3390/app15073758/sl](https://www.mdpi.com/article/10.3390/app15073758/sl)), ML_models.xlsx (includes sheets 4.1, 4.2, 4.3, 4.4 and 4.5). Conceptualization, C.-M.R.