Load Clustering Based on Variational Modal Decomposition and Spatiotemporal Network Variational Auto-encoder
In order to solve the problems of high data dimension,difficult feature extraction,and signal modal aliasing in the se-quence,a feature extraction algorithm for power load curve based on variational mode decomposition(VMD)and improved spatiotempo-ral networks variational auto-encoder(VAE)was proposed.First of all,the intrinsic mode of the signal was obtained by modal decom-position,and the sequence signal with obvious timing characteristics was obtained by modal reconstruction.Then,the spatiotemporal net-works variational auto-encoder composed of the long short-term memory network(LSTM)and the convolutional neural network(CNN)was used to extract potential features,and a network classifier was constructed to jointly associate the loss-optimized autoencoder model.Fi-nally,the Minibatchkmeans algorithm was used to cluster and calculate the clustering center.Using the actual electricity consumption of Portuguese residents in the UCI dataset as the experimental data,The experimental results show that the algorithm of dimensionality re-duction reclustering after modal decomposition has good results on the Davies-Bouldin index(DBI)and silhouette coefficient(SC).
万方数据
维普
