Author(s): Pereira, Clayton Reginaldo [UNESP] ; Passos, Leandro Aparecido [UNESP] ; Rodrigues, Douglas ; de Souza, André Nunes [UNESP] ; Papa, João P. [UNESP]
Date: 2020
Persistent ID: http://hdl.handle.net/11449/201219
Origin: Oasisbr
Subject(s): Adaptive differential evolution; Energy theft detection; Feature selection; JADE; Adaptive differential evolution; Adaptive differential evolution; Energy theft detection; Energy theft detection; Feature selection; Feature selection; JADE; JADE
Made available in DSpace on 2020-12-12T02:27:07Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-01
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Nowadays, non-technical losses, usually caused by thefts and cheats in the energy system distribution, are among the most significant problems an electric power company has to face. Several actions are employed striving to contain or reduce the implications of the conducts mentioned above, especially using automatic identification techniques. However, selecting a proper set of features in a large dataset is essential for successful detection rate, though it does not represent a straightforward task. This paper proposes a modification of JADE, an efficient adaptive differential evolution algorithm, for selecting the most representative features concerning the task of computer-assisted non-technical losses detection. Experiments on general-purpose datasets also evidence the robustness of the proposed approach.
School of Sciences UNESP - São Paulo State University
Department of Computing UFSCar - Federal University of São Carlos
School of Sciences UNESP - São Paulo State University
FAPESP: 2013/07375-0
FAPESP: 2014/12236-1
FAPESP: 2016/19403-6
FAPESP: 2017/02286-0
CNPq: 307066/2017-7
CNPq: 427968/2018-6
