Research on User Behavior and Electricity Usage Profile Based on Spectral Clustering Algorithm
This article analyzes user electricity consumption behavior through spectral clustering algorithm,aiming to generate accurate user profiles and optimize power resource allocation.Firstly,collect and preprocess user electricity consumption data,construct a similarity matrix to quantify the similarity of electricity consumption behavior among users.Next,calculate the Laplacian matrix of the similarity matrix and perform eigenvalue decomposition.Select the top k eigenvectors as inputs and use the K-means clustering algorithm to divide users into different behavioral groups.By analyzing the clustering results,generate characteristic portraits of electricity consumption behavior for each group.The research results indicate that spectral clustering algorithm can effectively identify different patterns of electricity consumption behavior,which is helpful for power companies to conduct demand forecasting,energy-saving services,and load management,and improve the overall efficiency and user satisfaction of the power system.