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Título: | Fraud detection on power grids while transitioning to smart meters by leveraging multi-resolution consumption data |
Autor: | Massaferro Saquieres, Pablo Di Martino, Matías Fernández, Alicia |
Tipo: | Artículo |
Palabras clave: | Feature extraction, Smart meters, Companie, Inspection, Energy consumption, Deep learning, Meters, Non-technical losses, Electricity theft, Automatic fraud detection, Multi-resolution, Smart meters |
Cobertura geográfica: | Uruguay |
Fecha de publicación: | 2022 |
Resumen: | The technological upgrade of power utilities to smart metering is a process that can take several years. Meanwhile, smart meters coexist with previous generations of digital and electromechanical power meters. While the smart meters provide high-resolution power measurements, electromechanical meters are typically read by an operator once a month. The coexistence of these two technologies poses the challenge of monitoring non-technical losses (NTL) and fraud where some customers’ consumption is sampled every 15 minutes, while others are sampled once a month. In addition, since companies already have years of monthly historical consumption, it is natural to reflect how the past data can be leveraged to predict and improve NTL on smart grids. This work addresses both problems by proposing a multi-resolution deep learning architecture capable of simultaneously training and predicting input consumption curves sampled 1 a month or every 15 minutes. The proposed algorithms are tested on an extensive data set of users with and without fraudulent behaviors collected from the Uruguayan utility company UTE and on a public access data set with synthetic fraud. Results show that the multi-resolution architecture performs better than algorithms trained for a specific type of meters (i.e., for a particular resolution). |
Editorial: | IEEE |
EN: | IEEE Transactions on Smart Grid, vol. 13, no 3, May 2022, pp. 2381-2389. |
Financiadores: | Este trabajo fue apoyado en parte por la empresa de servicios públicos uruguaya UTE y por la Comisión Académica de Posgrado de la Universidad de la República |
DOI: | 10.1109/TSG.2022.3148817 |
ISSN: | 1949-3053 |
Citación: | Massaferro Saquieres, P., Di Martino, M. y Fernández, A. "Fraud detection on power grids while transitioning to smart meters by leveraging multi-resolution consumption data" [Versión Aceptada]. Publicado en : IEEE Transactions on Smart Grid, vol. 13, no 3, May 2022, pp. 2381-2389. DOI: 10.1109/TSG.2022.3148817 |
Licencia: | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
Aparece en las colecciones: | Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | ||
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MDF22.pdf | Versión aceptada | 2,74 MB | Adobe PDF | Visualizar/Abrir |
Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons