###### Abstract
[13] highlight using daily wind speed data with machine learning algorithms to forecast wind power, reinforcing the versatility of ML applications in this field. [PERSON] et al. [14] developed multilayer feed-forward neural network (MLFFNN), support vector regression (SVR), fuzzy inference system (FIS), ANFIS, group method of data handling (GMDH) type neural network, ANFIS optimized with particle swarm optimization algorithm (ANFIS-PSO), and ANFIS optimized with genetic algorithm (GA), namely (ANFIS-GA), models to predict the time-series wind speed data. Moreover, data-driven methodologies are increasingly recognized for their ability to leverage large datasets for improved forecasting. For instance, a study by [PERSON] et al. [15] introduces a multi-model approach that employs deep feature selection to enhance short-term wind forecasting accuracy. This is complemented by the work of [PERSON] et al. [16], who utilize LSTM networks to outperform other regression algorithms in forecasting wind power output based on real wind speed data. The integration of advanced data processing techniques, such as WT, has also been explored, as demonstrated by [PERSON] et al. [17], who employ extreme learning machines in their forecasting model. [PERSON] et al. [18] employed Bayesian optimization (BO) to optimally tune hyperparameters of the Gaussian process regression (GPR), SVR with different kernels, and ensemble learning (ES) models (i.e., boosted trees and bagged trees) and investigated their forecasting performance. The use of ensemble learning techniques has also gained traction in wind forecasting. [PERSON] et al. [19] discuss the application of ensemble learning-based models to predict wind power, highlighting the advantages of combining multiple predictive models to enhance accuracy. This is further supported by [PERSON] et al. [20], who found that Gradient Boosting Regression Tree algorithms yield superior performance in forecasting wind power outputs compared to traditional methods. [PERSON] and [PERSON] [21] investigated the use of machine learning ensembles for wind power prediction and proposed a heterogeneous ensemble approach utilizing both DT and SVR. Such ensemble approaches capitalize on the strengths of various models, leading to more robust predictions. Along with the popular machine learning methods, the seasonal autoregressive integrated moving average (SARIMA) model stands out as a reliable method for wind speed prediction by effectively capturing seasonal trends and providing accurate forecasts essential for various meteorology and renewable energy management applications. Its adaptability and proven performance across different contexts make it a valuable tool for researchers and practitioners in the field. (SARIMA) model has emerged as a prominent statistical technique for wind speed prediction, mainly due to its ability to account for seasonal variations inherent in meteorological data. SARIMA extends the ARIMA model by incorporating seasonal components, making it particularly effective for time series data that exhibit periodic fluctuations, such as wind speed [22; 23]. The model's structure allows it to weigh historical observations differently, giving more importance to recent data, which is crucial for accurately forecasting rapidly changing environmental conditions [22]. Several studies have demonstrated the effectiveness of SARIMA in wind speed forecasting. For instance, [PERSON] et al. [24] highlighted that the SARIMA model outperformed traditional ARIMA models in monthly wind velocity forecasting, showcasing its enhanced accuracy. Similarly, [PERSON] et al. [25] compared SARIMA with deep learning models and found that while both approaches have their merits, SARIMA remains a robust choice for wind speed prediction due to its statistical foundation and interpretability. [PERSON] [26] applied long-term solar and wind resources estimation methods and indicated SARIMA as a powerful tool for prediction.