Analysis of user electricity behavior based on improved K-Means clustering algorithm

Abstract

Data mining and clustering analysis based on a large amount of user electricity consumption data have become important means to improve the effectiveness of demand response scheduling. This article aims to conduct in-depth research on user electricity consumption behavior based on smart grid data using data mining and clustering analysis methods. Firstly, a large amount of user electricity consumption data is collected, and relevant features related to user electricity consumption behavior are extracted. Then, data mining techniques such as clustering analysis are applied to divide users into different groups. By comparing the electricity consumption characteristics and behavioral habits among different groups, the differences in electricity consumption between different user categories can be revealed. The results of this study are of great significance to power supply companies in implementing demand response scheduling, optimizing resource allocation, and improving the utilization of electric power energy. By fully mining user electricity consumption data under the smart grid, a decision-making basis can be provided for power supply companies and promote the sustainable development of the power industry.