基于用户行为分析的双聚类窃电检测模型

Under the background of big data,a series of power theft behaviors of residents not only cause significant economic losses but also bring incalculable security risks.According to the unique characteristics of residential users,a dual clustering detection model of power theft is proposed in this paper based on analysis of users' power consumption behavior.First,by establishing a preliminary diagnosis model of power theft based on improved K-means clustering,shortcomings of the original K-means algorithm,such as determination of the K value and initial cluster center,low calculation efficiency,and vulnerability to noise,are improved.Second,based on power consumption char-acteristics of residential users,their power consumption behavior patterns are classified,11 types of residential power consumption patterns are summarized,and the final set of power thieves is determined by curve similarity analysis.Simulation results show the proposed model can significantly improve detection rate,false detection rate,and other evaluation indicators for the dataset of residential users at the same time,which provides a reference for power theft detection in power supply companies.