The short-term correlation of electricity consumption behavior is the important decision basis for user meticulous management and intelligent distribution. In this paper

a network method is first proposed to analyze the correlation matrix of electricity sequences. In order to eliminate the effects of noise information from the random behavior and measurement error

random matrix theory is adopted for correlation matrix denoising

and regularization correction exploiting eigenvalue spectrum entropy is also introduced. Then

according to the tiered and peer network sequences constructed from the filtered correlation matrix

a correlation clustering method based on community structure and a hierarchy mining method based on minimum spanning tree are proposed for detailed analysis

including topological indicators to characterize the dynamic evolution of correlation network

as well as the discussion of the model application. Case studies with instance data verify the validity of the proposed denoising method

network analysis model and indicators.