TY - GEN
T1 - Improving electricity non technical losses detection including neighborhood information
AU - Massaferro, Pablo
AU - Marichal, Henry
AU - Martino, Matias Di
AU - Santomauro, Fernando
AU - Kosut, Juan Pablo
AU - Fernandez, Alicia
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/21
Y1 - 2018/12/21
N2 - Non technical losses (NTL) cause significant damage to power supply companies' economies. Detecting abnormal clients behavior is an important and difficult task. In this paper we analyze the impact of considering customers geo-localization information, in automatic NTL detection. A methodology to find optimal grid sizes to compute a set of local features with a random search procedure is proposed. The number and size of the grids, and other classification algorithm parameters are adjusted to maximize the area under receiver operating characteristic curve (AUC), showing performance improvements in a data set of 6 thousand of Uruguayan residential customers. Comparative analysis with different sub-sets of characteristics, that include the monthly consumption, contractual information and the new local features are presented. In addition, we probe that raw customers' geographical location used as an input feature, gives competitive results as well. In addition we evaluate a entire new database of 6 thousand Uruguayan customers, whom were inspected in-site by UTE experts between 2015 and 2017.
AB - Non technical losses (NTL) cause significant damage to power supply companies' economies. Detecting abnormal clients behavior is an important and difficult task. In this paper we analyze the impact of considering customers geo-localization information, in automatic NTL detection. A methodology to find optimal grid sizes to compute a set of local features with a random search procedure is proposed. The number and size of the grids, and other classification algorithm parameters are adjusted to maximize the area under receiver operating characteristic curve (AUC), showing performance improvements in a data set of 6 thousand of Uruguayan residential customers. Comparative analysis with different sub-sets of characteristics, that include the monthly consumption, contractual information and the new local features are presented. In addition, we probe that raw customers' geographical location used as an input feature, gives competitive results as well. In addition we evaluate a entire new database of 6 thousand Uruguayan customers, whom were inspected in-site by UTE experts between 2015 and 2017.
UR - http://www.scopus.com/inward/record.url?scp=85060799814&partnerID=8YFLogxK
U2 - 10.1109/PESGM.2018.8586146
DO - 10.1109/PESGM.2018.8586146
M3 - Contribución a la conferencia
AN - SCOPUS:85060799814
T3 - IEEE Power and Energy Society General Meeting
BT - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018
PB - IEEE Computer Society
T2 - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Y2 - 5 August 2018 through 10 August 2018
ER -
