Obtaining accurate wind speed forecast result plays a decisive role in ensuring the reliable operation of the power system integrated with large-scale wind power. Deep learning methods are increasingly being used to predict wind speed, which have relatively high prediction accuracy but more time-consuming training processes. The purposes of this study are further to improve the prediction accuracy of the wind speed and reduce the training time of the deep learning method. In this paper, a novel hybrid model, New Cell Update Long Short-Term Memory combined with Empirical Wavelet Transform, is proposed to increase the prediction accuracy in shorter training time. At first, the original wind speed sequence is preprocessed into a series of sub-sequence by the empirical wavelet decomposition. Then each sub-sequence is trained by New Cell Update Long Short-Term Memory which is proposed by this paper respectively and the sum of each sub-sequence is treated as the final prediction results. In order to verify the performance of the proposed model, different decomposition methods and different prediction methods are compared on the four actual wind speed prediction cases in the Inner Mongolia, China from prediction accuracy and training time. The results demonstrate that: (1) New Cell Update Long Short-Term Memory network has slightly higher prediction accuracy and shorter training time than Long Short-Term Memory network. (2) The prediction accuracy of the model is significantly improved after empirical wavelet decomposition. Therefore, New Cell Update Long Short-Term Memory network combined with empirical wavelet decomposition is a competitive wind speed prediction method compared to the existing state-ofthe-art approach.