Title :
What is the importance of selecting features for non-technical losses identification?
Author :
Ramos, Caio C O ; Papa, João P. ; Souza, André N. ; Chiachia, Giovani ; Falcão, Alexandre X.
Author_Institution :
Dept. of Electr. Eng., Univ. of Sao Paulo, Sao Paulo, Brazil
Abstract :
Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles.
Keywords :
electricity supply industry; fraud; particle swarm optimisation; pattern classification; power consumption; classification accuracy; commercial profiles; feature selection algorithm; frauds; identification accuracy; industrial profiles; nontechnical losses automatic identification; nontechnical losses identification; optimum-path forest; particle swarm optimization; representative features; Accuracy; Bismuth; Libraries; Particle swarm optimization; Power systems; Prototypes; Training;
Conference_Titel :
Circuits and Systems (ISCAS), 2011 IEEE International Symposium on
Conference_Location :
Rio de Janeiro
Print_ISBN :
978-1-4244-9473-6
Electronic_ISBN :
0271-4302
DOI :
10.1109/ISCAS.2011.5937748
