# Application of Artificial Intelligence in Wind Power Systems
## 1 Introduction
It is therefore very important to predict the energy production of wind farms as well as possible on an hourly and daily basis. This production forecast now heavily depends on the advanced application of artificial intelligence (AI) to accurately predict wind speed and power [4; 5]. AI refers to the ability of computer applications to mimic human intelligence when performing complex tasks and to act autonomously by analyzing data from the environ  
Figure 1: Global installed wind power capacity [1]. ment to achieve specific goals. Digital technological developments have the potential to significantly improve energy supply and energy trading (stabilize prices). In the foreseeable future, the integration of supply, demand and renewable energy sources into the electricity grid will be controlled by intelligent software that optimizes decision-making in connection with system operation. Artificial intelligence will play a key role in achieving this goal [6]. The problem at issue here is at the level of the energy system. However, if we move to the level of a wind farm or a single wind turbine, then we are faced with the problem of optimizing the operation of a single wind turbine and maximizing production for given wind characteristics (speed, power, direction, turbulence), but also ensuring the reliability and highest possible availability of the wind turbine. In other words, maintaining existing power plants [7] and minimizing the environmental impact of wind turbines (reducing collisions with birds and bats [8]) are of great importance, and the application of artificial intelligence in this area is becoming increasingly important. If we analyze the topic of wind farms further, we come to the design phase, in which the individual components of the wind farm are determined. Each of the components should be optimized so that the wind farm produces the highest possible performance for a given site given the input data, but also has the lowest possible investment costs and the lowest possible impact on the environment. The optimal design of rotor blade profiles through rapid iterations of profile variants is enabled by the application of AI, together with the application of other tools related to computer simulations of rotor blade aerodynamics. AI can also play an important role in the end-of-life phase of wind turbines by planning the optimal decommissioning process, recycling and material recovery. AI can assess the condition of ageing turbines and predict their remaining lifespan, helping to decide whether they should be refurbished, upgraded or decommissioned. The extent of AI applications in wind energy has been analyzed by [PERSON] et al. [9], [PERSON] [10] and [PERSON] et al. [11], who examined patents related to wind turbine technology, patents related to AI and patents covering both wind turbines and artificial intelligence. [PERSON] et al. found that the number of patents in both areas increased significantly from 2010 to 2021, but that the overlap is quite small, meaning that there is still much room for progress in this area. Secondly, the patterns of interaction of AI and wind power technology knowledge show a shift from machine learning models (wind power technology on the production side) to deep learning models (wind power technology on the production, transmission and distribution side) to hybrid AI models (generation, transmission, distribution and energy consumption in the whole process of wind power technology). Many studies have analyzed trends and opportunities for AI applications in wind energy forecasting, wind energy control, wind farm design, maintenance and optimization. For example, [PERSON] et al. [12] summarized recent advances in hybrid AI methods for wind energy forecasting based on the literature. [PERSON] et al. [13] gave a comprehensive overview of AI and machine learning (ML) methods for wind turbine control. [PERSON] et al. [14] analyzed artificial intelligence algorithms in wind farm control and optimizations applications.