Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network

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 sub-layers; 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.