# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Application of Artificial Intelligence in the Design of Wind Turbines
#### 3.1.3 Location Selection and Environmental Impact Assessment  
Artificial intelligence (AI) is becoming increasingly significant in the selection of sites for wind turbines and the assessment of their environmental impact. Traditional methods are often time consuming and require a significant amount of manual work, while AI enables faster, more efficient and more precise analysis of large amounts of data. Geographic information systems (GIS) play a crucial role in mapping the suitability of wind turbines, as they facilitate the analysis and visualization of different spatial data layers that influence the potential for wind farm development. GIS is often combined with multi-criteria decision analysis (MCDA) techniques such as the analytic hierarchy process (AHP) to evaluate and prioritize sites based on different criteria. This approach allows stakeholders to weight factors according to their importance, resulting in a comprehensive suitability map that highlights optimal locations for wind energy projects. In general, the application of AI in site selection includes the following:  
* Analysis of meteorological data: UI algorithms can analyze huge amounts of meteorological data (wind speed and direction, temperature, humidity, etc.) collected from weather stations, satellites and other sources. This makes it possible to accurately predict the wind potential at different locations and identify optimal areas for the installation of wind turbines. AI can recognize patterns and trends in data that are not immediately visible to the human eye, leading to better estimates of a site's energy potential. For example, in their study, [PERSON] et al. [37] used a geographic information system (GIS) and the multi-criteria AHP to conduct a spatial analysis of the suitability of sites for wind turbines in East African countries. The authors gathered and analyzed data from various freely available sources, including the Regional Centre for Mapping of Resources for Development (RCMRD), the African Geoportal, the Global Wind Atlas, energydata.info and the East African Community website. In his research, [PERSON] [38] uses wind speed data from meteorological stations to predict the available wind energy at a given location. The analysis employed advanced deep learning techniques, including long short-term memory (LSTM), convolutional neural networks (CNNs), recurrent neural networks (RNNs), gated recurrent units (GRUs), as well as hybrid models such as LSTM-GRU, CNN-LSTM, CNN-GRU and CNN-RNN. Among these, the CNN-GRU model demonstrated superior performance, achieving a peak accuracy of 99.81% in wind energy prediction. The author used two different models and five different measurement systems to evaluate performance and reports that a high accuracy of 89% was achieved. As a result of this study, weekly, monthly, and annual predictions were made. * Analyzing topographical data: AI is used to analyze topographic maps and digital terrain models to assess the suitability of the terrain for wind turbines. Algorithms can identify areas with favorable characteristics, such as open areas with minimal wind obstacles, and can avoid areas with excessive slopes or other topographical constraints. An example is the study by [PERSON] et al. [39]. The study used a method combining GIS with multi-criteria decision analysis (MCDA) to identify suitable sites for onshore wind farms in southern France. In addition to the environmental factors mentioned above, the study also considered technical and social factors in the site selection process. * Integration of geographical information (GIS): UI can be integrated with GIS systems to combine different data layers, e.g., meteorological data, topographical data, infrastructure data (roads, power lines), protected areas, settlements and other relevant factors. This enables a comprehensive analysis of the area and the identification of optimal locations, taking all relevant aspects into account.