Short-Term Wind Power Forecast Based on CNN&LSTM-GRU Model Integrated with CEEMD-SE Algorithm - Guohua YANG; Xin QI; Rui JIA; Yifeng LIU; Fei MENG; Xin MA; Xiaowen XING (Zhongguo dianli)
In order to further improve the accuracy of short-term wind power forecast, a CNN & LSTM-GRU based short-term wind power prediction model using CEEMD-SE algorithm is proposed. First, the original wind power output series are decomposed into several intrinsic mode function components and one residual component by complementary set empirical mode decomposition, and those components of similar mode are reconstructed by sample entropy algorithm. Next, the parallel network structure of convolutional neural network and long short term memory network is set up, and the local and temporal features of the data are extracted. And then the features are fused and input into the gated cyclic unit network for learning and prediction. Finally, the feasibility of the model is verified through case studies. The results show that the forecast accuracy has been improved effectively. The root mean square error and average absolute error, of the proposed model are reduced by 15.06% and 15.22% respectively, while coefficient of determination is up by 1.91%.