###### Abstract
Recent studies have shown that AI methods, which reveal the best prediction results in most cases, are one of the main interests in the topic of wind speed and wind power estimation. Many studies on wind speed and wind power estimation. Many studies on wind speed and wind power estimation have been conducted using AI methods such as artificial neural networks (ANNs), deep learning, and machine learning. In studies by [PERSON] et al. [21] and [PERSON] et al. [22], a feedforward ANN is implemented to predict daily wind speeds using meteorological measurement data. [PERSON] and [PERSON] [23] introduced a Generalized Regression Neural Network (GRNN) and Multi-Layer Perceptron (MLP) for long-term wind speed estimation. It has been shown that the GRNN gives a better result than the MLP as a result of the comparison of these two models using the mean square error (MSE) metric [23]. [PERSON] et al. [24] developed a wind speed prediction model using different ANNs, such as the Multi-Layer Perceptron Neural Network (MLPNN) and Radial Basis Function Neural Network (RBFNN). [PERSON] et al. [25] performed wind power prediction based on daily wind speed data for different locations by using various machine learning algorithms, namely Least Absolute Shrinkage Selector Operator (LASSO) Regression, k-Nearest Neighbor Regression, Extreme Gradient Boost Regression, Random Forest Regression (RFR), and Support Vector Regression (SVR). [PERSON] et al. [26] proposed a topographic machine learning-based wind speed estimation model for the evaluation of onshore wind power potential and presented the comparison of the developed terrain-based ANN model and the Support Vector Machine (SVM). Various research papers have been published on the prediction of wind speed and wind power using hybrid approaches that combine statistical techniques, AI models, machine learning algorithms, deep learning algorithms, and optimization algorithms. Hybrid approaches that exploit the advantages of different techniques provide improved prediction accuracy and performance. [PERSON] et al. [27] presented a hybrid algorithm composed of the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Neural Network (NN) for multi-step-ahead wind speed forecasting using explanatory variables. The suggested hybrid algorithm was verified by comparing it with other well-known techniques in the literature, such as the NN, SARIMA, and SARIMA + Wavelet. [PERSON] et al. [28] utilized a short-time-period wind speed forecasting method based on Variational Mode Decomposition--ANN (VMD-ANN). [PERSON] et al. [29] introduced a short-term wind power forecast model, Statistical Hybrid Wind Power (SHWIP), and compared it with well-known models such as the ANN and SVM in the literature. [PERSON] et al. [30] addressed a prediction model combined with an NN, Particle Swarm Optimization (PSO), and SVR. [PERSON] et al. [31] employed three machine learning algorithms called a Multi-Layer Feed-Forward Neural Network (MLFFNN), SVR with a Radial Basis Function (SVR-RBF), and the Adaptive Neuro-Fuzzy Inference Systems (ANFISs) optimized with PSO (ANFIS-PSO) to estimate the wind speed, wind direction, and output power of a wind turbine. [PERSON] et al. [32] adopted a hybrid intelligence learning-based ANFIS to estimate the Weibull PDF with available wind speed data, and then compared the results with five well-known methods: the GM, the Empirical Method of Justus (EMJ), the Empirical Method of Lysen (EML), the Method of Moments (MOM), and the EPFM. [PERSON] et al. [33] introduced an AI optimization approach based on the Chebyshev metric to estimate the Weibull PDF parameters. This algorithm was then evaluated together with four different AI optimization algorithms (the Grey Wolf Optimizer (GWO), Bat Optimization (BOA), the Genetic Algorithm (GA), and the Artificial Bee Colony (ABCOA)) to obtain the optimum Weibull distribution parameters. [PERSON] et al.