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With the integration of wind energy into electricity grids, wind speed forecasting plays an important role in energy generation planning, power grid integration and turbine maintenance scheduling. This study proposes a hybrid wind speed forecasting model to enhance prediction performance. Variational mode decomposition (VMD) was applied to decompose the original wind speed series into different sub-series with various frequencies. A least squares support vector machine (LSSVM) model with the pertinent parameters being optimized by a bat algorithm (BA) was established to forecast those sub-series extracted from VMD. The ultimate forecast of wind speed can be obtained by accumulating the prediction values of each sub-series. The results show that: (a) VMD-BA-LSSVM displays better capacity for the prediction of ultra short-term (15 min) and short-term (1 h) wind speed forecasting; (b) the proposed forecasting model was compared with wavelet decomposition (WD) and ensemble empirical mode decomposition (EEMD), and the results indicate that VMD has stronger decomposition ability than WD and EEMD, thus, significant improvements in forecasting accuracy were obtained with the proposed forecasting models compared with other forecasting methods.The physical model relies on the information stated in the numerical weather forecast [5]. This model was established with many complicated factors such as pressure, temperature, obstacles and roughness, which are usually difficult to utilize in practical applications [6]. The traditional statistical model based on the mature statistical equations to obtain the potential evolution rule [7,8]. The most commonly used traditional statistical models for wind speed forecasting include the autoregressive model (AR) [9], autoregressive moving average (ARMA) [10] and autoregressive integrated moving average model (ARIMA) [11]. Liebl [12] proposed a new statistical perspective using a functional factor model for modeling and forecasting electricity spot prices that accounts for the merit order model. Statistical models have simple principles and high efficiencies. However, the prediction accuracy of low-order statistical models is relatively low, while high-order model parameters are tremendously difficult to obtain. As for the AI methods, artificial neural networks (ANN) [13], support vector regression (SVR) [14], regularized extreme learning machine (RELM) [15] and Least square support vector machines (LSSVM) [16] might be the most frequently used models for wind speed forecasting, and empirical analysis shows that they are superior to traditional linear models. Yeh [17] proposed a parameter-free simplified swarm optimization for ANN training for time-series prediction and demonstrated its robustness and efficiency. employed the SVR model and their results showed that the proposed model was more accurate than the persistence and auto-regressive models in medium short-term wind speed and wind power forecasting. Zhou et al. [16] built a LSSVM based model for one-step ahead wind speed forecas...