Load forecasting based on improved VMD and clustering weight sharing
A load prediction model based on improved variational mode decomposition(VMD)and clustering weight sharing is proposed for the problems that the common combined load prediction method of data decomposition plus prediction algorithm has many parameters,slow training and ineffective extraction of common information between modes.The model first introduces the cross-correlation function to find the optimal decomposition K value of VMD,then uses K-means to cluster the decomposed modal components to highlight the temporal characteristics of the modal components,and finally uses the idea of weight sharing to model the clustered components for fast and accurate prediction.The simulation results show that the mean absolute error and root mean square error of the model are 5.29％and 986.50,respectively,which are 7.50％and 982.41 lower on average compared with the traditional single prediction model.Compared with the common combination of data decomposition plus prediction algorithm,the mean absolute error and root mean square error of the proposed algorithm are 3.09％and 268.93 lower on average.There is also a certain improvement in the training speed.
load forecastingvariational modal decompositionweight sharingK-means clusteringlong and short-term memory networks
NETL
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