Defect Data Mining of Power Consumption Law Based on Improved K-Means Algorithm Clustering

Abstract

With the further construction and development of the smart grid, in the process of power development, production, and use, as well as the process of power distribution and use, each link will produce some high-dimensional data on the power grid with huge volume, complex structure, and complex correlation among them. The distribution of high-dimensional data in space is different from that in low-dimensional space, and the computational cost increases dramatically, which increases the complexity of visualization of high-dimensional power consumption data. Clustering analysis is a way to cluster a large number of users and summarize the typical load characteristics of different types of users. How to determine the prior information conditions of data and how to select the clustering criteria become the key to clustering. Aiming at the problems of traditional clustering algorithms in the current feature clustering analysis, this paper first deals with the load through t-SNE dimensional reduction technology, then combines the GSA elbow criterion and dichotomous K-means algorithm to cluster the load, and finally summarizes three typical load features according to the clustering results. Effective data mining technology is used to cluster and divide the massive load characteristics efficiently, which will dynamically respond to and manage the demand side. The error of classification results is less than 4.28% through the example of load characteristics. The classification accuracy of the test is 12.2% higher than that of the traditional method. According to the experimental results, the characteristics of typical load patterns and the corresponding load curve characteristics are analyzed. It overcomes the dependence of the traditional K-means algorithm on the initial centroid, avoids the algorithm falling into local optimum, and plays an important role in the defect data mining of power consumption law in power enterprises.