Research on user abnormal power consumption based on non-intrusive detection

Abstract

With the rapid increase in the types of electrical appliances used by users, the number of electrical accidents caused by this has increased year by year. In view of this, real-time monitoring of the use and operation of electrical appliances of power users is of great significance to better protect the safety of life and property of power users. Considering the monitoring cost and the acceptability of the monitored users, this paper proposes a machine learning fusion model based on V-I trajectory characteristic curve from the perspective of non-intrusive load decomposition, which realizes the effect of remote monitoring of abnormal electrical appliances with lower cost and higher user acceptance.