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Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network
- Liu, Hui, Mi, Xiwei, Li, Yanfei
- Energy conversion and management 2018 v.166 pp. 120-131
- models, prediction, time series analysis, wavelet, wind power, wind speed
- 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.