# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 6 Discussion
### The Impact of Discoveries on the Improvement of Wind Energy and Future Solutions  
The analysis of the specialized literature on the use of ML for optimizing wind energy has highlighted a series of aspects that can contribute to improving the performance of wind turbines and their integration into global energy systems. One primary aspect concerns optimizing energy production forecasts. DL and ANN models have demonstrated an improved ability to predict energy production based on meteorological and operational data. CNN and LSTM algorithms provide short-term predictions. This feature reduces uncertainty in energy planning. Using random forest, gradient boosting, and XGBoost to analyze turbine power curves allows for assessing future production. This allows for efficient energy production management in decisions regarding storing surplus energy. ML models can help increase operational efficiency through predictive maintenance. This allows for the early detection of turbine failures, directly reducing downtime and maintenance costs. Integrating IoT elements and SCADA data analysis allows for real-time turbine performance monitoring. This allows for the adjustment of parameters to optimize the process. Classification and anomaly recognition algorithms help optimize preventive maintenance strategies. Thus, the lifespan of the turbines is extended. Relevant application solutions target hybrid technologies previously identified in the literature. For example, hybrid ML modeling with CFD can be used to estimate turbine efficiency based on atmospheric conditions. Implementing digital twins technology to test turbine optimization scenarios without interfering with the physical infrastructure is also recommended. The authors also recommend exploring the possibility of using synthetic data in evaluating wind energy efficiency. Another hybrid approach could be integrating cloud technologies to transmit and process data from the turbines. Multi-criteria planning allows for optimizing energy resources by introducing a two-phase model and a GSR/RGSG heuristic algorithm for managing resource conflicts [150,151]. This proposal is applied to the planning of hybrid wind systems characterized by the uncertainty of global resources. At the same time, [PERSON] et al. [27] explore the optimization of maintenance costs for wind-photovoltaic hybrid systems. The simulations presented by [PERSON] et al. [152] conducted in MATLAB/Simulink demonstrate the ability to maintain stable frequency-voltage support for hybrid wind farms under variable conditions. Developing new strategies for integrating wind energy into grids can be associated with intelligent storage systems (batteries, power-to-gas, green hydrogen) to compensate for wind intermittency [153]. [PERSON] et al. [154] explored the potential of gravitational storage. These technologies offer eco-friendly and cost-effective solutions for storing excess wind energy. Additionally, the paper by [PERSON] et al. [155] discussed the role of the complete hydrogen-based energy chain in decarbonization. These approaches highlight the importance of integrating multi-energy systems to streamline the energy storage process. In identifying the current limitations, it is explicitly mentioned that an interdisciplinary approach facilitates the energy transition toward a higher level of technological optimization. The convergence of AI, energy engineering, meteorology, and economic sciences can provide hybrid solutions for:  
* Intelligent management of renewable resources through production optimization;
* The implementation of digital twin technology in energy networks;
* Enhancing wind turbine efficiency through predictive maintenance;
* Optimizing wind energy production through adaptive control;
* Integrating renewable sources into smart grids using advanced forecasting models;
* Improving energy storage efficiency with ML-based technologies that analyze demand and supply in real-time.