## 2 Literature Review  
Wind speed forecasting models are usually classified into physical, statistical, machine learning, and hybrid models [5]. The physical models usually employ the weather data such as meteorological features and geographical information to forecast wind speed. The numerical weather prediction (NWP) method is one of the prominent general-purpose physical models that returns satisfactory results. [PERSON] et al. [6] use a well-known NWP method, weather research and forecast (WRF), to asses different numerical and physical options. [PERSON] et al. [10] combine the NWP with the Gaussian process regression for improved surface wind speed predictions. However, updating frequencies of predictions and computational requirements are the main obstacles of the NWP methods. Statistical methods are extensively utilized in short-term wind speed forecasting. [PERSON] et al. [11] use the autoregressive (AR) model to forecast wind speed in their pioneer work. [PERSON] et al. [12] utilize the ARMA (autoregressive moving average process) method to forecast the average of hourly wind speed for 10 h ahead. [PERSON] and [PERSON] [13] use the same method to forecast wind speed. Their results indicate that their model achieves accurate speed prediction within one hour. [PERSON] [14] proposes an ARIMA (autoregressive integrated moving average) model that uses ten minutes and one-hour averages to forecast the next one-hour wind speed. The results show that ten minutes averages achieve better accuracy. [PERSON] and [PERSON] [15] study fractional-ARIMA models to predict wind speed within two days ahead of time. [PERSON] et al. [16] employ ARIMA to forecast wind power generation and present the improvements. [PERSON] et al. [17] propose a hybrid model employing the ARIMA and clustering methods to forecast wind speed for one year in advance. [PERSON] et al. [18] introduce a seasonal ARIMA model for short-term offshore wind speed forecast. They compare the performance of the ARIMA model to machine learning algorithms, the GTU (gated recurrent unit), and the LSTM (long short-term memory). Their results imply that the seasonal ARIMA model is more capable than the GTU and the LSTM. [PERSON] et al. [19] compare univariate ARIMA and NARX (nonlinear autoregressive exogenous) models. The presented results show that the NARX outperforms the ARIMA in wind speed forecasting. Artificial intelligence algorithms gain popularity in wind speed forecasting due to the presence of nonlinearity in wind data. In particular, ANN (artificial neural network) is a powerful tool for handling nonlinear data [8]. [PERSON] and [PERSON] [20] study ANN models to forecast wind speed for short-term. They indicate that two-layer ANN is the best for both the training and forecasting stages. [PERSON] and [PERSON] [21] use the FANN (feedforward neural networks) model with the inputs of past data and daily wind characteristics. [PERSON] et al. [22] utilize CNN (convolutional neural networks) to handle high dimensional data in wind forecasting. They propose a CNN-based feature extraction method to compress high-dimensional NWP results. Another study [23] employs CNN to take into account temporal and spatial changes of wind to forecast wind power generation. [PERSON] et al. [24] utilize an RNN (recurrent neural network) algorithm (LSTM) to forecast the short-term wind energy production in Estonia. Their results show that LSTM is superior to SVR (support vector machines) and NAR (Nonlinear Autoregressive Neural Networks). Hybrid and combined models exploit the advantages of various models for better accuracy and computational time in wind speed forecasting. For instance, [PERSON] et al. [25] adopt wavelet transform in forecasting to eliminate high-frequency data and SVR. [PERSON] et al. [26] propose a two-level method to increase the accuracy of wind power prediction. At the first level, wind speed time data are decomposed into sublayers using WPD (Wavelet Packet Decomposition).