## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Method** & **Variables** & **References** \\ \hline Wind and Solar Energy & D2C causality model & coal consumption, economic growth, and environmental pollution & [11] \\ Wind and Solar Energy & IoT, SVM, NB, and GBDT & wind speed, temperature differences, and atmospheric conditions & [16] \\ Wind Energy & ANN, SVM, and K-NN & past wind energy generation & [17] \\ High-Altitude Wind & VT, NN, GB, DTR, NR, LR, Energy Harvesting & kite position, kite orientation, towing speed (km/h), and tether force (N) & [18] \\ Wind Energy & DRL & battery energy storage & [19] \\ Wind Energy & DL and Taguchi design & technical and meteorological variables & [20] \\ Wind Energy & NN, SVR, SVM, and DL & wind speed and other related & [21] \\ Wind Energy & ANN and CFD simulations & meteorological parameters & [22] \\ Wind and Solar Energy & GPR & structural and technical & [23] \\ Wind Energy & GB, kNN, RF, Tree, LR, and ANOVA & time, environmental, technical, and & This research \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The subject, method, and variable information of some studies. focusing on reducing wind energy costs, reducing risks, and improving performance with machine learning methods aimed at providing more accurate estimates and lower error rates [16]. A study offers significant contributions to energy management and planning by developing a hybrid model that combines machine learning algorithms in a dynamic structure, providing high accuracy in short- and long-term wind energy forecasts and reducing the error rate to lower levels than other models in the literature (5.54% NMAE--Normalized Mean Absolute Error) [17]. Another study introduced Kyushu University's kite system for high-altitude wind energy harvesting and trained machine learning models with experimental data. It developed an artificial neural network model that accurately predicted the rope force generated by the kite [18]. A study proposed a planning model that integrates wind power forecasting and energy storage system (ESS) decision-making processes to maximize the revenues of wind power producers and established an ESS controller by considering wind power uncertainty using a deep Q-network algorithm [19]. Another study developed a deep learning-based model optimized with the Taguchi experiment that can simultaneously capture the time dynamics and the relationship between local meteorological variables in wind power plants to make short-term forecasts for power generated from wind turbines, and this model shows consistent improvements over existing methods at all wind speeds [20]. [PERSON] et al. aimed to use wind energy more efficiently by making medium-term wind speed forecasts to provide maximum efficiency to wind power plants in India. [PERSON] et al. present an experimentally validated simulation-based technology that can rapidly and accurately predict the energy losses of wind turbine blade leading edge erosion using machine learning and show that this technology evaluates the annual energy loss of turbines for various erosion severities [22]. In another study, to make short/medium-term wind and solar energy forecasts for reliable and strategic planning in the energy sector, a Gaussian stochastic-based machine learning model (GPR) was tested with four different fitting and estimation methods using four experimental steps (EXMS) using data with varying resolutions of time [23]. This study proposes a two-stage method based on time, environmental, technical, and locational variables for wind energy production prediction. In the first stage, the predicted values for the dependent variable (wind energy production) were obtained using machine learning algorithms. In the second stage, the ANOVA test was applied to understand the effect of these predicted values on the independent variables. This method evaluates the predictions' accuracy, the prediction results' robustness, and their relationship with the independent variables.