A novel framework from Nontechinical Losses Detection in Electricity Companies
Resumen:
Nontechnical losses represent a very high cost to power supply companies, who aims to improve fraud detection in order to reduce this losses. The great number of clients and the diversity of different types of fraud makes this a very complex task. In this paper we present a combined strategy based on measures and methods adequate to deal with class imbalance problems. We also describe the features proposed, the selection process and results. Analysis over consumers historical kWh load profile data from Uruguayan Electricity Utility (UTE) shows that using combination and balancing techniques improves automatic detection performance.
| 2013 | |
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Electricity theft Support vector machine Optimum path forest Unbalance class problem Combining classifier UTE |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/47029 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
