# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Application of Artificial Intelligence in the Operation of Wind Turbines
#### 3.2.1 Improved Accuracy of Forecasting Electricity Production and Consumption  
It can be said that wind energy forecasting, although important, is not considered a key area for the greatest contribution of AI in wind farms for several reasons:  
* Wind energy production is highly variable and is influenced by numerous unpredictable factors such as wind speed, wind direction, temperature and air pressure. This inherent instability makes accurate forecasting difficult and often leads to significant fluctuations between predicted and actual energy production. * The complexity and randomness of these variables can limit the effectiveness of AI models in producing reliable forecasts [46]. * Significant progress has already been made in the field of wind energy forecasting using traditional meteorological methods and numerical weather prediction models. Many operators are effectively utilizing these proven techniques, which may reduce the current need for AI-driven solutions compared with other areas, such as predictive maintenance, where AI can provide more transformative benefits. * As wind energy is increasingly integrated into power grids, generation and demand need to be balanced. While accurate forecasting is critical to this, it is primarily a tool rather than a transformative application of AI. The focus is often on optimizing grid management rather than improving the fundamental operational capabilities of the wind farms themselves. However, analyzing and monitoring turbine performance in real time using AI has significant advantages. In response to changing wind conditions, AI algorithms can dynamically adjust turbine settings, such as blade pitch and nacelle rotation angles, to maximize energy production. As a result, energy production increases and overall efficiency is higher. According to some studies, AI can improve the efficiency of wind turbines by up to 20% [47]. Below are some of the most important research and applied AI methods related to wind power generation forecasting. AI algorithms are excellent at analyzing huge data sets, including historical weather patterns, turbine performance metrics and real-time environmental conditions. By processing this information, AI can produce highly accurate power generation forecasts based on expected wind conditions [46]. This allows operators to optimize energy use and manage grid stability more efficiently, reducing the risk of power outages or inefficiencies due to imbalances between supply and demand. Precise short-term wind energy forecasting plays a critical role in mitigating the challenges associated with voltage peaks and frequency regulation within the power grid, as well as the connection of wind farms to the electricity grid. Some research work has been carried out in this context. For example, [PERSON] et al. [48] propose a conventional neural network algorithm based on machine learning to forecast production and calculate power production forecast errors. They used real data from a 40 kW wind farm at a site in Rajasthan. In their study, [PERSON] et al. [49] introduce a novel hybrid model that strategically integrates multiple complementary machine learning techniques to enhance the precision of wind turbine energy production forecasting. The ensemble learning (EL) approach demonstrates superior performance compared with long short-term memory (LSTM), light gradient boosting machine (LightGBM), and sequenced-gated recurrent unit (Sequenced-GRU) in predicting wind energy. The proposed model achieves an exceptional R\({}^{2}\) value of 0.9821, underscoring its high level of accuracy. In their study, [PERSON] et al. [50] propose several artificial intelligence techniques to simulate the rotation speed of the turbines and predict the wind energy production 10 min in advance.