关键词: 完备总体经验模态分解, 小波变换, 卷积神经网络, 短期风速预测, 固有模态分量, 二次去噪

Abstract: Because there are randomness and uncertainty in wind speed, this paper proposes a hybrid model of Complete Ensemble Empirical Mode Decomposition（CEEMD）, Wavelet Transform（WT） and Convolutional Neural Networks（CNN） to improve forecasting accuracy. Firstly, CEEMD decomposes original wind speed into some relatively stable intrinsic mode functions and a residual sequence. Then, WT makes secondary noise elimination to eliminate effects of noise on each intrinsic mode function. Finally, the final result is obtained by refactoring forecasting results that CNN trains each intrinsic mode function, residual sequence and five attribute to obtain respectively. Compared with other four wind speed forecasting model, the Mean Absolute Percentage Error（MAPE） is 2.484% in the proposed model. This indicates that model of CEEMD-WT-CNN exists better performance in terms of short-term wind speed forecasting.

Key words: Complete Ensemble Empirical Mode Decomposition（CEEMD）, Wavelet Transform（WT）, Convolutional Neural Networks（CNN）, short-term wind speed forecasting, intrinsic mode function, secondary noise elimination