Título:
Fraud detection using event logs with LSTM and gradient boosting.
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Autor:
Acevedo, Emiliano
Massaferro Saquieres, Pablo
Fernández, Alicia
Martins Masner, Alexander
Caudullo, Gonzalo
Massaferro Saquieres, Pablo
Fernández, Alicia
Martins Masner, Alexander
Caudullo, Gonzalo
Tutor:
Tipo de documento:
Ponencia
Editor:
Palabras clave:
Energy consumption
Recurrent neural networks
Costs
Time series analysis
Energy resolution
Feature extraction
Particle measurements
Recurrent neural networks
Costs
Time series analysis
Energy resolution
Feature extraction
Particle measurements
Descriptores:
Año de publicación:
2023
Contenido:
Resumen:
Automatic non-technical power loss detection methods have advanced significantly as data volume has increased with smart meter installation. Recently, academic works have mainly focused on the impact of the high resolution of the energy consumption time series, leaving aside the integration of event logs within machine learning solutions. Due to the variety of alarms and depending on electrical installation health, millions of alarm events can be generated requiring an automatic analysis of them. In this work, we propose a method that considers the sequential nature of alarm log information using a recurrent neural network and evaluate two strategies for including this information within an existing state-of-the-art NTL classifier. The experiments are reported in actual smart meter data provided by the Uruguayan utility, showing that it is possible to double the precision for on-field applicable operating thresholds.
Descripción:
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EN:
2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 16-19 jan. 2023, pp. 1-5.
Financiadores:
Los autores agradecen a UTE por financiar el proyecto, así como por proporcionar los conjuntos de datos y compartir su experiencia sobre el problema.
Citación:
Acevedo, E., Massaferro Saquieres, P., Fernández, A. y otros. Fraud detection using event logs with LSTM and gradient boosting [en línea]. EN: 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 16-19 jan. 2023, pp. 1-5.
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Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Tratamiento de Imagenes
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| AMFMC23.pdf | Camera Ready | 822.1 KB | Adobe PDF |