Electricity Theft Detection Base on Extreme Gradient Boosting in AMI

Metering data from the advanced metering infrastructure can be used to find abnormal electricity behavior for the detection of electricity theft, which causes huge financial losses to electric companies every year. This article proposes an electricity theft detector using metering data based on extreme gradient boosting (XGBoost). The metering data are preprocessed, including recover missing and erroneous values and normalization. The classification model based on XGBoost is trained using both benign and malicious samples. Simulations are done by using the Irish Smart Energy Trails data set with six certain attack types. Compared with the support vector machine, decision tree, and other eight machine learning methods, the proposed method can detect electricity theft with either higher accuracy or lower false-positive rate. Experiment results also demonstrate that the proposed method is robust when the data are imbalanced. Our codes are available at https://github.com/WenHe-Hnu/Electric_Theft_XGBoost.

Automated summary

This article introduces an electricity theft detector based on extreme gradient boosting (XGBoost), which preprocesses metering data and trains a classification model to detect abnormal electricity behavior. Experimental results show that the method can detect electricity theft with higher accuracy or lower false-positive rate.