# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 6 Discussion
### Recommendations for Future Research  
Future research should focus on expanding datasets, exploring geographic and turbine height impacts, and developing standardized evaluation metrics for ML models in wind energy optimization. To increase the number of research studies on optimizing wind energy production processes, the datasets should contain meteorological data from wind farms that include the following features:  
* Wind speed and direction at different altitudes;
* Temperature and atmospheric humidity;
* Atmospheric pressure;
* Turbulence and extreme phenomena (storms, gusts of wind);
* Short-term and long-term forecasts for climate variations. Currently, these datasets are either incomplete, do not belong exclusively to a specific park, or do not contain a sufficiently large volume of data to provide templates that MLmodels can identify. Information about the terrain's topography is extracted from features such as the following:  
* The altitude and geographical structure of the area;
* Natural or artificial obstacles that can influence airflow;
* The distribution of turbines in wind farms;
* The impact of the wake effect;
* Operational data regarding network integration. A few specialists cannot collect such datasets because they involve multidisciplinary collaboration. These data can only be collected from the wind farm, which is unavailable in the public version. The difficulty in accessing such a dataset hinders research in the field. Regarding the technical parameters of the turbines, they are characterized by the following:  
* The power produced depends on the weather conditions;
* The efficiency of turbines at different wind speeds;
* Data regarding component wear;
* The degree of necessity of maintenance;
* Malfunctions detected;
* The operating duration of the turbines. If there were possibilities to recombine the set in the case of datasets associated with meteorological data or terrain topology, the work would be complicated in the case of technical parameters. A data operator cannot recombine such a dataset, as it can only be extracted from the actual process. Future work and prospects in wind energy optimization using ML should consider expanding the field by simultaneously addressing the factors influencing wind power production. Exploring hybrid models should combine multiple ML techniques and extend the research to models that have not yet been sufficiently studied in wind energy. The authors recommend studying the fluid dynamics of wind turbines. Another recommendation from the authors is to publicly distribute data from wind farms so that researchers can study the data. It is well-known that ML models require large volumes of data to identify patterns. Additionally, a series of studies could be explored using synthetic data at the digital twin level. This combination could be a virtual replica of wind turbines that simulate real-world conditions. Another idea could be to improve ML models by training them on quantum systems. This study could identify accuracies that have not been achieved in previous training. Additionally, the training could be repeated periodically by adding new data and incorporating the effects of recent decisions. Wind energy optimization should be studied concurrently with other renewable energies, such as wind-solar systems. Another interdisciplinary direction through which optimization studies can be conducted involves IoT technology. This technology consists of installing sensors on wind turbines. They report the monitored values in real time through a Cloud infrastructure. The integration of ML models allows for decision-making to optimize production performance dynamics. The energy storage method can be implicitly identified from the category of these decisions. Therefore, future research should focus on interdisciplinary collaboration. The next generation of hybrid AI models should enable real-time optimization. The evolution of wind energy production optimization is closely correlated with technological advancements in AI and electrical engineering.