# 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
Four tools are used for the prediction: The fuzzy C-means (FCM) approach of the adaptive neuro-fuzzy inference system (ANFIS), the long-term memory (LSTM), the grid-partitioning (GP) method of the adaptive neuro-fuzzy inference system and the subtractive clustering (SC) algorithm of the adaptive neuro-fuzzy inference system. These methods use historical data as input for the physical parameters to be estimated and estimate the subsequent value as output. It has been shown that LSTM performs best when capturing real, observed wind turbine parameters, while the ANFIS-FCM model provides the most accurate results for wind energy. [PERSON] [51] has introduced an artificial neural network (ANN)-based methodology for short-term wind energy prediction, utilizing a swarm intelligence algorithm for optimization. The study includes a simulation of a real-world wind power system in Turkey under varying wind speeds, conducted using MATLAB/Simulink. The swarm intelligence algorithm is employed to fine-tune the parameters of the forecasting model. The proposed algorithm's effectiveness is assessed using actual data from a wind farm in Turkey. Three distinct approaches are applied for efficient data processing: ANN, ANN integrated with the firefly algorithm (ANN-FA), and ANN combined with particle swarm optimization (ANN-PSO). The findings demonstrate that the swarm intelligence algorithm surpasses conventional prediction methods, including statistical approaches and standard machine learning techniques, in terms of both accuracy and reliability. [PERSON] et al. [52] used artificial intelligence to attempt to predict electricity generation from wind energy one day in advance. The project was implemented on two SCADA data sets that provided different parameters. The first dataset provided information on wind speed, wind direction, theoretical power and active power. The output variable was energy production (KWh). The second data set provided data on power output over time. The article proposes and compares different machine learning methods and neural networks for predicting energy from wind energy. Five common machine learning regressor algorithms and long-term short-term models were compared, focusing on the target-output variable,and an approach for forecasting weather series in a scenario with insufficient weather parameters for wind data was proposed. In their research, [PERSON] et al. [53] created data-driven models for wind speed and power prediction by employing machine learning (ML) and deep learning (DL) techniques, incorporating site-specific climatological data. Additionally, they designed an advanced recommendation system to optimize turbine placement and identify ideal locations for power plants based on wind strength and speed. The study highlights the real-time effectiveness of the proposed method, with XGBoost and random forest regressors achieving 94% accuracy and an average percentage error of 6 in forecasting 15-day power output. A similar study to predict wind energy production was conducted by [PERSON] and [PERSON] [54]. They developed a model using the decision tree, random forest, K-nearest neighbor (KNN) and XGBoost algorithms. The dataset was sourced from real-time SCADA data obtained from wind turbines, allowing for a comprehensive analysis. The results of this study confirm the effectiveness of machine learning methods, especially XGBoost, in accurately predicting wind power generation and at the same time emphasize the importance of computational efficiency in practical implementation. [PERSON] et al. [55] investigated the ways to estimate regional wind energy production and proposed a multi-aggregate model of wind energy characteristics based on three scaled Gumbel distribution functions for regional aggregated wind energy. The relative peak power and full load hours were compared for the proposed model and the actual measurements of the local distribution system operator using artificial intelligence models using neural networks such as long short-term memory (LSTM), compound LSTM and CNN-LSTM.