Short-Term Load Forecasting Based on CEEMDAN-SE-VMD and CNN-BIGRU
Targeting at the problem of low accuracy in electricity load forecasting,a load forecasting method based on CEEMDAN-SE-VMD and CNN-BIGRU combined model is proposed.Firstly,the model first applies complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)to decompose the complex raw load data.,and several intrinsic mode functions(IMF)with different frequency components are obtained after decomposition.Then sample entropy(SE)is used to cluster the decomposed intrinsic mode functions with different frequencies.Then,variational mode decomposition(VMD)is used to decompose the reconstructed high frequency sequences.The sub-sequences obtained from the quadratic decomposition and the low frequency sequence and trend sequence data reconstructed by sample entropy(SE)are input into CNN network to extract the high-level feature vectors reflecting the complexity and correlation of load sequences.Finally,it is input into the bidirectional gated recurrent unit(BIGRU)network for prediction,the prediction results of each subsequence are obtained,and the final load sequence prediction results are superimposed.Through the comparison of transverse and lon-gitudinal experimental results,it is proved that the proposed model can improve the accuracy of power load prediction.
万方数据
维普
