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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A Probabilistic Optimum-Path Forest Classifier for Non-Technical Losses Detection

Full text
Author(s):
Fernandes, Silas E. N. [1] ; Pereira, Danillo R. [2] ; Ramos, Caio C. O. [3] ; Souza, Andre N. [4] ; Gastaldello, Danilo S. [4] ; Papa, Joao P. [5]
Total Authors: 6
Affiliation:
[1] Univ Fed Sao Carlos, Dept Comp, BR-13565 Sao Carlos, SP - Brazil
[2] Univ Western Sao Paulo, Inst Informat, BR-19065 Presidente Prudente - Brazil
[3] Catarinense Fed Inst, Dept Elect Engn, BR-89163356 Rio Do Sul - Brazil
[4] Sao Paulo State Univ, Dept Elect Engn, BR-17033360 Bauru - Brazil
[5] Sao Paulo State Univ, Dept Comp, BR-17033360 Bauru - Brazil
Total Affiliations: 5
Document type: Journal article
Source: IEEE TRANSACTIONS ON SMART GRID; v. 10, n. 3, p. 3226-3235, MAY 2019.
Web of Science Citations: 1
Abstract

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. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants
FAPESP's process: 17/02286-0 - Probabilistic models for commercial losses detection
Grantee:André Nunes de Souza
Support Opportunities: Regular Research Grants
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants
FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support Opportunities: Research Projects - Thematic Grants