A Probabilistic Optimum-Path Forest Classifier for Non-Technical Losses Detection
Probabilistic-driven classification techniques extend the role of traditional approaches that output labels (usually integer numbers) only. Such techniques are more fruitful when dealing with problems where one is not interested in recognition/identification only, but also into monitoring the behavi...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/185671 |
| Acceso en línea: | http://dx.doi.org/10.1109/TSG.2018.2821765 http://hdl.handle.net/11449/185671 |
| Access Level: | acceso abierto |
| Palabra clave: | Optimum-path forest probabilistic classification non-technical losses |
| Sumario: | Probabilistic-driven classification techniques extend the role of traditional approaches that output labels (usually integer numbers) only. Such techniques are more fruitful when dealing with problems where one is not interested in recognition/identification only, but also into monitoring the behavior of consumers and/ or machines, for instance. Therefore, by means of probability estimates, one can take decisions to work better in a number of scenarios. In this paper, we propose a probabilistic-based optimum-path forest (OPF) classifier to handle the problem of non-technical losses (NTL) detection in power distribution systems. The proposed approach is compared against naive OPF, probabilistic support vector machines, and logistic regression, showing promising results for both NTL identification and in the context of general-purpose applications. |
|---|