Power consumer subdivision method based on combined matrix decomposition model

Abstract

The invention discloses a power consumer subdivision method based on a combined matrix decomposition model. The method comprises the steps: inputting user power utilization record data, and constructing a user power utilization record matrix; inputting the geographical position information of the user, representing the geographical position information through hierarchy, constructing a user geographical position information similarity matrix, and regulating the weights of different parts in the geographical position information at different hierarchies; constructing a target function of the combined matrix decomposition model according to the user power utilization record matrix, and selecting a reasonable target function solving algorithm for solving, so as to obtain the power utilization demands of users; and carrying out the subdividing of the users according to the power utilization demands of the users. Compared with a conventional method, the method enables the representation of the users in a demand space to be more abundant in meaning. The method employs a clustering algorithm, integrates the characteristics of the users in a demand hiding space and the relation among the users for clustering of the users, and enables the correlation of users in each cluster to be closer. The power utilization demands and geographic positions of the users of different clusters are more different.

Description

technical field [0001] The invention belongs to the technical fields of computer application technology, data mining and electric power data analysis, and in particular relates to a method for subdividing electric power users based on a joint matrix decomposition model. Background technique [0002] With the improvement of power grid informatization level, a large amount of data is generated in the power system, which also brings new challenges to the analysis of power data. Traditional power data analysis focuses on the research of data generated in production and power supply links, and the analysis of user data is often aimed at all users, thus ignoring the characteristics of users themselves and the relationship between users. User data usually includes information such as the user's electricity consumption behavior, geographic location, time, date, etc., but the current user data analysis, the more common method is to use clustering algorithms to classify users based on...

AI Technical Summary

Problems solved by technology

User data usually includes information such as the user's electricity consumption behavior, geographic location, time, date, etc., but the current user data analysis, the more common method is to use clustering algorithms to classify users based on electricity consumption records, and lacks information on other information. Comprehensive analysis, and this information often contains important factors that affect users' electricity consumption behavior

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