# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 2 Basic Concepts of the Wind Power and Turbines
### ML Techniques in the Wind Power Context  
ML algorithms are categorized into the three main types of supervised learning, unsupervised learning, and reinforcement learning, with each suited for different kinds of tasks and data structures. Supervised learning is used for classification tasks [44; 45]. In this case, the model learns from labeled training data to predict outcomes on unseen data. Common algorithms in this category include support vector machines (SVMs), K-nearest neighbors (KNNs), and various tree-based methods like decision trees and random forests [46; 47; 48; 49]. In contrast, unsupervised learning seeks to find the hidden patterns or intrinsic structures in unlabeled data, making it useful for clustering tasks and anomaly detection [50; 51; 52]. The paper by [PERSON] et al. [53] proposed the AMBi-GAN model for detecting anomalies in industrial multidimensional time series. The proposed model utilized LSTM mechanisms. The model was applied to wind turbines to detect faults. [PERSON] et al. [54] introduced a new algorithm called bagged trees that detects anomalies based on vibration analysis. The analysis was conducted to predict synthetic defects using ML techniques. The results provided an accuracy of 87%. Reinforcement learning focuses on learning optimal actions through trial and error within a given environment and has applications in dynamic decision-making situations [55]. The performance of ML algorithms is evaluated using metrics. These metrics are correlated with the implemented algorithm. The most popular metrics are accuracy, precision, recall, and F\({}_{1}\)-Score [56; 57; 58]. Accuracy measures the proportion of true results among the cases examined. Precision and recall provide insights into the model's ability to identify instances accurately [5]. The F\({}_{1}\)-Score balances these two metrics, making it particularly useful when dealing with imbalanced datasets, where one class may significantly outnumber the other [56; 57]. Naive Bayes is used in text classification and various probabilistic applications. Its foundational principle revolves around Bayes' theorem, providing a straightforward yet powerful classification approach. Studies have demonstrated its performance in numerous contexts, often surpassing other algorithms, such as KNN, in accuracy [59]. Algorithms such as AdaBoost and gradient boosting increase the prediction accuracy by combining multiple weak classifiers to create an intense overall model. XGBoost has gained acclaim for its performance in competitive data science contexts [60]. Techniques like principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are commonly employed for dimensionality reduction in handling large datasets. PCA seeks to reduce dimensionality while preserving as much variance as possible in the data. On the other hand, t-SNE is particularly effective for visualizing high-dimensional data in lower dimensions by capturing complex relationships between data points [61]. ML models are studied in the specialized literature to fulfill the following purposes with optimization implications [62]:  
* To optimize processes to maximize energy production;
* The prediction of meteorological events correlated with the prediction of energy production vs. network energy consumption;
* Ensuring the prediction of maintenance needs in wind farms. These objectives can be achieved using ML techniques that process large amounts of historical data, based on which predictions can be made [38]. In the past, the study of wind power optimization was limited to understanding mechanical and atmospheric phenomena [63] without implications of AI technologies. This modeling involved the analysis of nonlinear dynamics' processes. The adoption of these modern technologies simplifies the data analysis and modeling processes. A comparison between traditional modeling and ML-specific modeling is presented in the research [63; 64]. The works address the problem through artificial neural networks (ANNs) to forecast short-term wind power.