## 2 Literature Background  
Proposed models for wind energy prediction can be categorized as physical models, traditional statistical models, ML models, and other hybrid models [12]. In 2023, [PERSON] et al. proposed a theoretical model of an integrated system based on a conical Concrete-Filled Double Skin Tube (CFDST) wind turbine, aiming to investigate the dynamic behavior of the integrated system in the field of wind energy. A model was formulated utilizing the Lagrangian approach for the governing equation, and it was analytically solved through the Galerkin method. The developed model was validated with wind tunnel tests of scaled models, followed by extensive parametric discussions. This study resulted in a reliable analytical model capable of accurately predicting the fundamental frequency and displacement response, which has been corroborated [13]. Unlike physical models, statistical models use meteorological information to estimate wind speed and include a Numerical Weather Prediction (NWP) model to estimate wind speed [14; 15]. The Weather Research Forecast (WRF) model, which considers the intricacies of the terrain and the resolution of the chosen region, is also employed as an effective forecasting method for predicting wind speed. It has demonstrated favorable performance in short-term wind speed prediction [16]. The autoregressive integrated moving average (ARIMA) model is the most used traditional statistical model. It utilizes historical data to create a prediction model that effectively captures the linear relationship within the training dataset [14]. As statistical models and synthetic techniques have advanced, ML models have been introduced to enhance prediction models [17]. ML-based models, including artificial neural networks (ANNs), fuzzy logic (FL) methods, and support vector machines (SVMs), are extensively employed in wind speed prediction [16]. However, it is important to note that no single method is universally applicable to all datasets [18]. Consequently, hybrid models that combine traditional statistical models with ML models have been proposed to enhance the accuracy of wind speed prediction [12]. Numerous studies have focused on integrating individual prediction models to create improved prediction models. Nevertheless, a review of wind speed estimation studies has revealed a lack of universally accepted definitions for these approaches [12]. Additionally, there are other prediction models that combine different prediction methods, such as hybrid approaches that incorporate parameter optimization, dataset preprocessing techniques, and error handling techniques [18]. ML algorithms are designed to identify patterns and correlations within extensive datasets, enabling them to make optimal decisions and predictions through thorough analysis [19]. As machine learning applications gain access to larger volumes of data, their performance improves, and they become increasingly successful. These algorithms utilize the framework of regression analysis techniques to construct prediction models and determine the associations between variables [20]. Linear regression (LR) is a supervised learning algorithm in machine learning that predicts the value of a variable based on another variable. It utilizes LR calculators with the least squares method to determine the best-fit line for a set of paired data [21]. On the other hand, random forest (RF) regression improves prediction accuracy by utilizing the decision tree (DT) method multiple times, with decision trees being randomly selected subsets from the dataset [22]. XGBoost regression is a successful method based on the eXtreme Gradient Boosting decision tree algorithm [23]. Since eXtreme Gradient Boosting (XGBoost) uses the parallel working technique when revealing the decision tree, operations occur quickly. One of its most well-known and important capabilities is that it can use the observation point in the dataset according to its weight values to find the appropriate point when classifying data into many trees.