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Title	Decision Tree Based Electricity Theft Detection In Smart Grid
ID_Doc	17720
Authors	Tehrani S.O.; Moghaddam M.H.Y.; Asadi M.
Year	2020
Published	Proceeding of 4th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2020
DOI	http://dx.doi.org/10.1109/SCIOT50840.2020.9250194
Abstract	One aspect of using smart meters is detecting anomalies in advanced metering infrastructure. Electricity theft, as a well-known anomaly, can be discovered by various machine learning algorithms. In this paper, decision tree, random forest, and gradient boosting methods are implemented and performed on collected power consumption data from 114 single-family apartments to detect non-Technical loss, which are generated by various scenarios. Performances of these algorithms are analyzed with and without clustering on the measured data of each user. © 2020 IEEE.
Author Keywords	anomaly detection; decision tree; electricity theft; gradient boosting; random forest; smart grid

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