Load shape acquisition method and system

Abstract

The embodiments of the invention provide a load shape acquisition method and a system. The method comprises the steps: acquiring a power consumption load curve, and determining all possible cluster class numbers; carrying out fuzzy clustering on the power consumption load curve by taking each possible cluster class number as a cluster class number of a fuzzy C-mean clustering algorithm respectively, so as to obtain a clustering result respectively; evaluating all obtained clustering results by using predetermined validity indexes so as to obtain the optimal clustering result, and acquiring all load forms of the power consumption load curve according to the optimal clustering result. According to the method and the system, the applicability of power consumption load curve clustering results and the accuracy of extracted load forms are improved.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a load pattern acquisition method and system. Background technique [0002] The load shape analysis is mainly to classify the electricity load curves of a large number of users according to the user's electricity load characteristics, and extract the representative typical load curves of different types of users. In the research, the method of clustering is usually used to classify and distinguish the user's electricity load curve. [0003] However, at present, when performing load shape analysis, the determination of user load categories is basically based on the use of experience to judge the number of cluster categories, resulting in poor applicability of the obtained load classification (clustering) results, and low accuracy of the extracted load shape . Contents of the invention [0004] In view of this, the embodiments of the present invention provide a method and system ...

AI Technical Summary

Problems solved by technology

[0004] In view of this, the embodiments of the present invention provide a method and system for acquiring load patterns to solve the problem of using experience to determine the number of clustering categories in the prior art to determine the user load category, resulting in poor applicability of the load classification results. The problem of low accuracy of the load shape of the

Images