Social-Spider Optimization-based Support Vector Machines applied for energy theft detection
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- 作者：Danillo R. Pereiraa ; danilopereira@unoeste.br" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Mario A. Pazotia ; mario@unoeste.br" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Luí ; s A.M. Pereirab ; luismartinspr@gmail.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Douglas Rodriguesc ; douglasrodrigues.dr@gmail.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Caio O. Ramosd ; caioramos@gmail.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; André ; N. Souzad ; andrejau@feb.unesp.br" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Joã ; o P. Papa ; e ; papa@fc.unesp.br" class="auth_mail" title="E-mail the corresponding author ; papa.joaopaulo@gmail.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Nontechnical losses ; Power distribution systems ; Social-Spider Optimization ; Support Vector Machines
- 刊名：Computers & Electrical Engineering
- 出版年：2016
- 出版时间：January 2016
- 年：2016
- 卷：49
- 期：Complete
- 页码：25-38
- 全文大小：511 K
文摘
- •
ra0001">Social-Spider Optimization for model selection in Support Vector Machines.
- •
ra0002">Three distinct scenarios were evaluated.
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ra0003">Proposed approach validated in the context of of theft detection in power distribution systems.