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 nonfraud 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.
Authors in REF using machine learning techniques on the past data to built models for detecting anomalous meter readings.
Fraud Detection in Energy Consumption: A Supervised Approach
2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Computer Science