基于谱聚类算法的用户用电行为画像研究  

Research on User Behavior and Electricity Usage Profile Based on Spectral Clustering Algorithm

作　　者：尹志超 YIN Zhichao

机构地区：[1]国网湖北省电力有限公司黄石供电公司,湖北黄石430077

出　　处：《电力系统装备》2024年第7期172-174,共3页Electric Power System Equipment

摘　　要：文章通过谱聚类算法对用户用电行为进行分析,旨在生成精确的用户画像,优化电力资源配置。收集并预处理用户用电数据,构建相似度矩阵,以量化用户之间的用电行为相似性。计算相似度矩阵的拉普拉斯矩阵,并进行特征值分解,选取前k个特征向量作为输入,使用K-means聚类算法将用户划分为不同的行为群体。通过对聚类结果的分析,生成各群体的用电行为特征画像。研究结果表明,谱聚类算法能够有效识别不同用电行为模式,有助于电力公司进行需求预测、节能服务及负荷管理,提升电力系统的整体效率和用户满意度。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.

关 键 词：谱聚类算法 用户用电行为 用户画像 电力需求预测 

分 类 号：TP393.092[自动化与计算机技术—计算机应用技术]