Clustering fusion method based on user electrical load data subdivision

Abstract

A clustering fusion method based on user electrical load data subdivision comprises the following steps of (1) collecting data, (2) modifying the data, (3) converting the data, (4) standardizing the data, (5) constructing a pretreatment clustering algorithm set, (6) building a consensus matrix, (7) running the clustering fusion method, and (8) collecting users. Due to the facts that results obtained in different clustering algorithms or in the same clustering algorithm by using different parameters are automatically combined, and a best clustering result can be automatically judged and generated through clustering fusion, the clustering fusion method based on the user electrical load data subdivision has the advantages of improving self-adaption processing capability of a clustering analysis model, reducing dependency degree to priori knowledge in a user electrical load data clustering analysis process, reducing manual operations, and improving automatic degree of the clustering fusion method based on the user electrical load data subdivision.

Description

technical field [0001] The invention relates to the technical field of electric power system load modeling, in particular to a cluster fusion algorithm based on subdivision of user electric load data. Background technique [0002] User load data analysis is an important method for power supply companies to understand the characteristics of user load patterns, and plays an important role in power supply company planning load management, substation construction, and grid operation status evaluation. By studying the characteristics of users' electricity load patterns, it is helpful for power supply companies to understand users more deeply, and to formulate corresponding market strategies and provide targeted personalized services according to different user groups. [0003] The current mainstream research method for subdividing user groups based on user power load data is to use cluster analysis algorithms to intelligently group user power load data based on the shape changes ...

AI Technical Summary

Problems solved by technology

Since the user's electricity load data is an unbalanced time series data, the time series characteristics of the data structure seriously affect the effect of traditional clustering algorithms

In order to obtain a more reliable clustering effect, it is necessary to manually modify and modify the clustering model according to different analysis data and prior knowledge, resulting in poor applicability and robustness of the clustering model, and the analysis results rely on artificial operation, unable to achieve system hardening

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