Title : ( Decision Tree based Electricity Theft Detection in Smart Grid )
Authors: Mohammad Hossein Yaghmaee Moghaddam ,
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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.
Keywords
, anomaly detection , electricity theft , smart grid , decision tree , random forest , gradient boosting
@inproceedings{paperid:1083003,
author = {Yaghmaee Moghaddam, Mohammad Hossein},
title = {Decision Tree based Electricity Theft Detection in Smart Grid},
booktitle = {2020 4th International Conference on Smart City, Internet of Things and Applications (SCIOT)},
year = {2020},
location = {IRAN},
keywords = {anomaly detection
;
electricity theft
;
smart grid
;
decision tree
;
random forest
;
gradient boosting},
}
%0 Conference Proceedings
%T Decision Tree based Electricity Theft Detection in Smart Grid
%A Yaghmaee Moghaddam, Mohammad Hossein
%J 2020 4th International Conference on Smart City, Internet of Things and Applications (SCIOT)
%D 2020
