Semisupervised approach to non technical losses detection
Resumen:
Non-technical electrical losses detection is a complex task, with high economic impact. Due to the diversity and large number of consumption records, it is very important to find an efficient automatic method to detect the largest number of frauds with the least amount of experts hours involved in preprocessing and inspections. This article analyzes the performance of a strategy based on a semisupervised method, that starting from a set of labeled data, extends this labels to unlabeled data, and then allows to detect new frauds at consumptions. Results show that the proposed framework, improves performance in terms of the F measure against manual methods performed by experts and previous supervised methods, avoiding hours of experts/inspection labeling.
| 2014 | |
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Electricity fraud Support vector machine Semisupervised approach SVMlight TSVM Unbalance class problem Procesamiento de Señales |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://hdl.handle.net/20.500.12008/41831
https://doi.org/10.1007/978-3-319-12568-8_85 |
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| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| Sumario: | Non-technical electrical losses detection is a complex task, with high economic impact. Due to the diversity and large number of consumption records, it is very important to find an efficient automatic method to detect the largest number of frauds with the least amount of experts hours involved in preprocessing and inspections. This article analyzes the performance of a strategy based on a semisupervised method, that starting from a set of labeled data, extends this labels to unlabeled data, and then allows to detect new frauds at consumptions. Results show that the proposed framework, improves performance in terms of the F measure against manual methods performed by experts and previous supervised methods, avoiding hours of experts/inspection labeling. |
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