## 1 Introduction
### Existing Models  
Currently, developing an accurate and effective wind speed forecasting model is a priority in many countries. These models can be divided into four categories based on the specific time horizon: Very short-term, short-term, medium-term, and long-term forecasting [2]. Very short-term and short-term forecasting can provide aids and references when making economic load dispatch plans, while medium and long-term forecasting are employed for the maintenance of the grid, wind farm planning, and providing information for site selection [3; 4]. Using computational methods, these forecasting methods can be further categorized into four different types: Physical, statistical, intelligent, and hybrid methods. Physical methods are usually based on numerical weather prediction (NWP), which simulates the physical characteristics of the atmosphere through applying physical rules and geographical conditions, though there are still many difficulties in employing this model for wind speed forecasting directly--for example, the forecasting accuracy, resolutions of space and time, and the importance of the physical procedures. Statistical methods are used for wind speed forecasting through setting mathematical models, which are similar to the direct random time-series models [5]. As one of the most widely employed statistical methods, the Autoregressive Integrated Moving Average (ARIMA) model is adopted to predict wind speed. Moreover, an Autoregressive Fractional Integrated Moving Average (ARFIMA) model is used in wind speed prediction because of its superior ability to select valid information from the past time series more effectively, compared with the traditional ARIMA model. In recent years, intelligent approaches such as artificial neural networks (ANN) and back propagation neural networks (BP), with the aim of reducing errors by employing past time series, are being applied to forecast future wind speed because of the rising popularity of artificial intelligence [6; 7; 8; 9; 10; 11; 12]. Due to the complexity of the raw wind speed series, forecasting the traits in the time series accurately is very difficult to achieve with individual models. Therefore, hybrid models which adopt multiple approaches to increase the prediction ability of the raw time series, or which assemble multiple forecasting methods to obtain the traits of the raw data to forecast wind speed, have been proposed in many studies. [PERSON] et al. combined the Seasonal Autoregressive Intergrated Moving Average model (SARIMA) and the Least Square Support Vector Machine model (LSSVM) to obtain a more accurate forecasting model [13]. This new approach can effectively catch the seasonal and nonlinear features in the input data. Similarly, [PERSON] et al. used a back-propagation neural network, and relied on the theory of using a seasonal exponential adjustment to reduce seasonal influence from the raw data, to create a new hybrid approach [14]. Experimental results showed that this new model is able to effectively increase the forecasting precision, as compared to the results of an independent back propagation neural network (BPNN) with no adjustment of the seasonal exponent. From the studies of [PERSON] et al., a new and very short-term wind speed hybrid model was proposed and proven to significantly increase the prediction range [15; 16]. [PERSON] et al. combined Empirical Model Decomposition to create a new hybrid Empirical Mode Decomposition-Artificial Neural Network (EMD-ANN) model. The proposed approach was effective in capturing jumping samples in raw data with high noise [17]. [PERSON] et al. proposed a Stackelberg game technique to improve the efficiency of electric grid [18]. [PERSON] et al. introduced a new multi-time-scale approach modeled by historical time-series for electric data forecasting [19]. [PERSON] et al. put forward the idea of combining multiple soft computing models with a data preprocessing technique to perform short-term wind speed forecasting [20].