Analysis of Power Grid User Behavior Based on Data Mining Algorithms – System Design and Implementation

Abstract

A data mining based power grid user behavior analysis system has been designed to address the issues of insufficient stability and accuracy in existing power grid user behavior analysis systems. Design the overall structure of the power grid user behavior analysis system; In terms of system hardware design, select a core controller, build and install a server as the foundation for system information transmission and logical operation; Based on ZigBee wireless communication technology, a ZigBee wireless communication protocol stack and communication expansion board were designed; In terms of system software design, Python is used to crawl user behavior data in the system data collection layer, and Python language is used to maintain the crawling program; Use the K-means algorithm to perform secondary mining and clustering on power grid user behavior data, obtain the analysis results of power grid user behavior, and transmit them to the system visualization display layer. The weight and Rand coefficient of data analysis were used as indicators to test the application effect of the method in this paper. The experimental results showed that the system can stably and accurately analyze the behavior of power grid users, and has good application effect. This research achievement has important reference significance for the research in the field of power grid user behavior analysis in the world scientific community.