# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 5 Analyzing Influencing Parameters in Wind Energy with ML
### Detailed Literature Content Review of ML Applied in Wind Energy Optimization Process  
The 60 papers are organized into five categories as follows:  
* Twenty papers address wind energy and wind speed forecasting. These are divided as mentioned next (Figure 12): \(\circ\)
* Two papers conduct review-type analyses, noting that ML models are studied to optimize wind farm power generation and minimize downtime. [PERSON] et al. [88] explored ML applications in wind turbine control, covering wind speed and power prediction, mechanical fault detection, and electrical fault prevention. A novel physics-inspired neural network model [99] also integrated wake effects into wind power forecasting. [PERSON] et al. [99] increased prediction accuracy by over 20% compared to traditional models. This approach highlights the importance of considering wake effects in short-term forecasting;
* Five papers are specific forecasting studies. The study by [PERSON] and [PERSON] [100] used LSTM, XGBoost, and Bayesian-optimized for weather forecasting. A comparative ML analysis in [101] optimizes LightGBM, reducing RMSE to 190.34 kW. [PERSON] et al. [102] evaluated hydrogen production from suburban wind, showing that LSTM provides the best estimates. In Mongolia, the models improved wind forecasting accuracy, reducing errors by up to 36.19% [103]. [PERSON] et al. [104] presented a hybrid DL model for Baltic Sea wind speed forecasting in the context of wind energy. \(\circ\)
* Eight papers tackled wind energy production optimization from an ML perspective. [PERSON] and [PERSON] [105] analyzed the optimization of wind energy in Andhra Pradesh, India, using an optimized neural network model that achieved 97.1% accuracy. Another ML model was presented by [PERSON] et al. in the paper  
Figure 11: Articles by publishers in wind energy optimization. at [106] that enhances wind speed prediction through AI and proposes a new performance metric called AECP. According to [PERSON] et al. [107], ML- and LiDAR-based models optimize wind farm design. The ANN-Jensen model [108] improves the prediction of wake-induced power losses. In the paper by [PERSON] et al. [109], DWFE used combined ML models for dynamic wake estimation. The HHO-XGBoost model proposed by [PERSON] et al. [110] predicts offshore turbine vibrations. [PERSON] et al. [111] enhanced rotor speed prediction using LiDAR. The research by [PERSON] et al. [112] presented a data-driven method for predicting offshore turbine structural vibration accelerations using extreme gradient boosting; Four papers adopted hybrid approaches, combining technologies. [PERSON] et al. [113] introduced the DeepVELOX model for wind energy forecasting, achieving a MAPE of 0.0002 and an R\({}^{2}\) of 1. In South Africa, a hybrid model demonstrated the feasibility of distributed wind generation for agriculture. Short-term offshore wind speed forecasting using seasonal econometrics, autoregressive integrated moving average as a comparison with AI models is presented in the paper [114]. [PERSON] et al. [115] developed a hybrid physics-data-driven model, improving short-term wind energy forecasting by 16% through optimized predictor selection. A novel 48 h forecast model for small turbines used hybrid methods and neural networks, reducing errors by integrating seasonal and historical variables [116]. * Fifteen papers address wind turbine optimization and control via ML methods. These are categorized as stated below: \(\circ\) Two are review-type papers and focus on grid-connected wind farms. These discuss maximizing energy production and downtime. ML algorithms are analyzed for wind turbine control, wind speed forecasting, and fault detection [88; 117]; \(\circ\) Five papers are case studies conducting applied analyses on specific data. Advanced wind turbine optimization techniques include indirect control for tracking the maximum power point of doubly fed induction generators [118], ML-based modeling, and periodic pitch angle adjustment to improve energy production [119]. Also, in the paper by [PERSON] et al.