Low-voltage transformer area user power consumption data clustering method based on mean shift clustering

Abstract

The invention provides a low-voltage transformer area user power consumption data clustering method based on mean shift clustering, and belongs to the technical field of power consumption data unsupervised data clustering, and the method comprises the steps: giving a power consumption data set X belonging to Rm * n of an m-dimensional space, and calculating a density estimation function of user power consumption data xi (i = 1, 2,..., n); according to the extreme value theorem of the function, the local density maximum value point is located in the gradient zero value point of the density function, derivation is conducted on the formula, the gradient function is obtained, and therefore the maximum value point of the power consumption data density is found; influences of noise data in the power consumption data are considered, a Gaussian kernel function is introduced, high-dimensional separability of the user power consumption data is achieved, and classification robustness is improved. According to the low-voltage transformer area user electricity consumption data clustering method based on mean shift clustering, in a low-voltage transformer area electricity consumption information collection scene, user types are diversified, electricity consumption scenes and electricity consumption behaviors are complex, and the quality basis of electricity consumption data analysis is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of unsupervised data clustering of electricity consumption data, and particularly relates to a clustering method of electricity consumption data of users in low-voltage station areas based on mean shift clustering. Background technique [0002] With the popularization and application of smart meters, the interaction between power supply companies and users has become increasingly frequent, and the user-side power consumption data has grown rapidly, which has contributed to the development of user-side power big data. The classification and analysis of the power consumption data of power users can more accurately understand the power consumption behavior of different types of power users, and provide a basic basis for power companies to achieve high-quality and accurate services. However, in the current operation of the power system, the real information value of power big data has not been fully utilized, th...

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Problems solved by technology

However, in the current operation of the power system, the real information value of power big data has not been fully utilized, thus causing a waste of power big data resources

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