|
Título:
|
Fraud detection in energy consumption: a supervised approach
|
|
Autor/a:
|
Coma Puig, Bernat; Carmona Vargas, Josep; Gavaldà Mestre, Ricard; Alcoverro, Santiago; Martín, Victor
|
|
Otros autores:
|
Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals; Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
|
Abstract:
|
Data from utility meters (gas, electricity, water) is a rich source of information for distribution companies, beyond billing. In this paper we present a supervised technique, which primarily but not only feeds on meter information, to detect meter anomalies and customer fraudulent behavior (meter tampering). Our system detects anomalous meter readings on the basis of models built using machine learning techniques on past data. Unlike most previous work, it can incrementally incorporate the result of field checks to grow the database of fraud and non-fraud patterns, therefore increasing model precision over time and potentially adapting to emerging fraud patterns. The full system has been developed with a company providing electricity and gas and already used to carry out several field checks, with large improvements in fraud detection over the previous checks which used simpler techniques. |
|
Abstract:
|
Peer Reviewed |
|
Materia(s):
|
-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
-Machine learning
-Energy consumption
-Public utilities
-Data analysis
-Fraud
-Learning (artificial intelligence)
-Aprenentatge automàtic
-Energia -- Consum |
|
Derechos:
|
|
|
Tipo de documento:
|
Artículo - Versión presentada
Objeto de conferencia |
|
Editor:
|
Institute of Electrical and Electronics Engineers (IEEE)
|
|
Compartir:
|
|
