Method for mining dominant influence factors of power utilization behavior of user

Abstract

The invention discloses a method for mining dominant influence factors of a power utilization behavior of a user. The method comprises the steps of performing multiple correlation check on an influence factor data set; if multiple correlation exists, performing screening by adopting a stepwise regression method; judging whether a target data table has the dominant influence factors or not by applying typical correlation analysis; if the target data table has the dominant influence factors, performing clustering analysis on the target data table by adopting an improved K-center point clusteringalgorithm; and finally obtaining data about the dominant influence factors. According to the method, analysis of the power utilization behavior of the user is changed to variable analysis research from sample statistical classification, so that the influence factors of the power utilization behavior of the user can be better mined; secondly, K-center point clustering analysis is improved, namely,an ideal solution method is introduced for determining an initial clustering center, so that falling into a local optimal solution is avoided; clustering algorithm parallelization is realized, so that the data processing capability of the algorithm is remarkably improved; and finally, an output result intuitively displays spatial and temporal distribution characteristics of the dominant influencefactors in multiple forms.

Description

technical field [0001] The present invention relates to the field of electricity consumption behavior analysis, and more specifically, relates to a method for mining dominant influencing factors of user electricity consumption behavior. Background technique [0002] With the rapid development of the smart grid, the power consumption information collection system and distribution automation are gradually improved, and the user-side data of the power grid presents big data characteristics such as large data volume, multiple data types, and fast growth rate. In the big data environment, analyze the correlation between electricity consumption data, and mine the characteristics of user electricity consumption behavior hidden in the user electricity consumption data. The methods applicable to the analysis of user electricity consumption behavior include pattern recognition technology, cluster analysis method, and data mining algorithm. information, so that it can effectively supp...

AI Technical Summary

Problems solved by technology

The main defects of the existing clustering methods are as follows: (1) The current user electricity consumption behavior analysis is mainly to classify the electricity consumption data samples, without considering the influencing factors of user electricity consumption behavior; (2) The traditional partition clustering method randomly selects the initial The clustering center is easy to fall into a local optimal solution, and when faced with a large amount of data, the data processing ability of the algorithm drops sharply

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