# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 6 Discussion
### Research Gaps Identification and Study Limitations  
By analyzing the previous work, the following gaps in the research on ML for wind energy optimization have been identified:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML Model** & **Precision (Score)** & **Efficiency (Score)** & **Weakness** \\ \hline Linear Regression & 2 & 4 & Cannot model complex nonlinear relationships \\ Logistic Regression & 2 & 3 & Poor performance for multi-class and complex problems \\ KNNs & 3 & 2 & Computationally expensive for large datasets \\ SVMs & 4 & 3 & Slow training for large datasets \\ Decision Trees & 3 & 3 & Overfits easily without pruning \\ Random Forest & 4 & 4 & Hard to interpret, computationally expensive \\ Gradient Boosting, & 5 & 4 & Requires hyperparameter tuning, high computational cost \\ XGBoost, AdaBoost & & & Black-box nature requires large datasets \\ ANNs & 5 & 3 & The high computational power required \\ RNNs, LSTM & 4 & 3 & Long training times, vanishing gradient issue \\ DL–Hybrid models & 5 & 4 & It needs big data, computationally expensive \\ K-Means Clustering & 3 & 3 & Poor performance on noisy data \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of the identified ML models in the literature based on the wind energy optimization process.  
No studies specifically address the influence of geographic topology or turbine height on wind energy optimization. These factors help to understand the localized wind patterns and should be addressed in the literature as future research;
* The literature lacks standardized evaluation metrics for comparing ML models in wind energy optimization. This inconsistency complicates the assessment of model reproducibility of results;
* Clustering methods like K-Means and DBSCAN are underexplored in wind energy optimization. These techniques should be explored to identify patterns in wind turbine behavior;
* Most studies focus narrowly on forecasting wind speed or power output rather than addressing comprehensive production optimization. This fragmentation limits the development of unified optimization frameworks;
* Techniques like PCA and t-SNE are rarely used for dimensionality reduction in wind energy datasets. These methods could simplify complex data pattern recognition;
* Climatic conditions, terrain topology, and grid connectivity are underrepresented in the literature. These factors influence wind energy production and require deeper investigation;
* Many studies consistently fail to report key performance metrics (e.g., accuracy, F1-Score, AUC-ROC). This gap hinders the ability to compare and validate ML models effectively;
* Most studies focus on short-term forecasting and optimization, leaving long-term strategies underexplored;
* Hybrid approaches that combine physics-based models with ML techniques are rare. These models could better capture the complex dynamics of wind energy systems;
* The fragmented nature of the current research highlights the need for unified frameworks that integrate meteorological variables, historical data, and operational constraints for comprehensive optimization.  
The identified gaps highlight the need for standardized and interdisciplinary research in wind energy optimization using ML. Addressing these gaps could lead to advances in optimizing wind energy production.