## 1 Introduction  
Wind energy is vital in the energy sector because of its clean, renewable, and sustainable features [1]. Its potential to reduce carbon emissions and dependence on fossil fuels is increasing interest in wind energy. However, the efficiency of wind energy production is affected by many time, environmental, technical, and locational factors [2]. A detailed analysis of these factors is critical to increase the accuracy of energy production estimates and enable more effective planning of energy policies [3]. The efficiency of wind energy depends not only on wind speed but also on several factors, such as local weather conditions, terrain, wind turbine layout, and maintenance status [4]. Therefore, the accuracy of wind energy production estimates is of great importance for the safe and sustainable management of energy grids. Modern machine learning techniques, especially artificial intelligence-supported forecasting models, to minimize uncertainties in wind energy production and increase efficiency, play an important role [5]. These models allow more accurate energy production estimates based on environmental parameters such as wind speed, direction, and temperature, as well as historical production data and weather forecasts [6]. In addition, these advanced forecasting systems increase the predictability of fluctuations in energy production, allowing optimization of energy storage systems and demand management strategies [6, 7]. Such developments facilitate the integration of renewable energy sources into grids by enabling more reliable and efficient use of wind energy [8]. Wind energy production estimation presents various challenges due to its highly dynamic and complex structure [4]. In addition to meteorological and technical variables such as wind speed, direction, and rotor speed, time- and location-based features also significantly affect energy production. In this context, the use of machine learning algorithms to understand and model the relationship between these variables has become increasingly widespread in recent years [9]. Machine learning algorithms provide powerful tools for analyzing complex data structures and identifying nonlinear relationships [10]. Various studies have shown that machine learning algorithms effectively estimate wind energy production [11; 12]. In particular, algorithms such as random forest (RF), gradient boosting (GB), k-nearest neighbor (kNN), decision trees (Tree), and linear regression (LR) have the potential to increase prediction accuracy [13]. However, the dataset, variables, and analysis methods significantly affect these algorithms' performance and prediction accuracy [14]. In addition, the prediction results' reliability is evaluated with error metrics and statistical verification of the effect of independent variables [15]. The subject, method, and variable information of some studies on wind energy are given in Table 1. This table summarizes the topics of the various studies, the methods used, and the variables investigated. These studies analyze various meteorological and technical variables using machine learning techniques such as wind and solar energy, high-altitude wind energy harvesting, and energy storage systems. One study examines the causal relationships between renewable energy, coal consumption, economic growth, and CO\({}_{2}\) emissions in China, India, and the US, finding that while China and the US show potential to reduce emissions through solar and wind energy adoption, India faces challenges that require a more substantial transition away from coal to low-carbon sources [11]. Another article examines the use of IoT technologies in wind and solar energy assessments, module life estimations, and renewable energy forecasts,