High precision and reliable wind speed forecasting is important for the management of the wind power. This paper develops a novel wind speed prediction model based on the WPD (Wavelet Packet Decomposition), CNN (Convolutional Neural Network) and CNNLSTM (Convolutional Long Short Term Memory Network). In the proposed WPD-CNNLSTM-CNN model, the WPD is employed to decompose the original wind speed time series into a number of sub-layers; the CNN with 1D convolution operator is used to forecast the obtained high-frequency sublayers; and the CNNLSTM is adopted to complete the forecasting of the low-frequency sub-layer. To verify and compare the prediction performance of the proposed model, eight models are used. According to the results of four experimental tests, it can be observed that: (1) the proposed model is robust and effective in predicting the 1D wind speed time series, besides, among the involved eight models, the proposed model can perform best in wind speed 1-step to 3-step predictions; (2) when the wind speed experiences sudden change, the proposed model can have better prediction performance than the other involved models.