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

A technology of influencing factors and users, applied in marketing, data processing applications, instruments, etc., can solve the problems of not considering the influencing factors of users' electricity consumption behavior and the decline of algorithm data processing capabilities

Active Publication Date: 2018-09-28
GUANGDONG POWER GRID CO LTD
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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 con...

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

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Embodiment Construction

[0060] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0061] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0062] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0063] Such as figure 1 As shown, a method for mining the dominant influencing factors of user electricity consumption behavior includes the following steps:

[0064] S1: collect the user's power data, the power data includes power consumption data and influencing factor data;

[0065] S1.1: Use the smart meter to collect the electricity consumption data of the user for a certain period of time. The electricity consumption data includes current and power; define the set of electricity consumption data as the dependent variable data table B, and record the data in...

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q30/02G06Q50/06
CPCG06Q30/0201G06Q50/06G06F18/23213G06F18/24Y02D10/00
Inventor 黄剑文彭泽武周珑萧展辉蔡徽徐晖钱正浩严宇平江疆
Owner GUANGDONG POWER GRID CO LTD
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