Colombian Energy Market: Optimal Portfolio

This paper focuses on the study of an optimal portfolio in the Colombian Energy Market using the Artificial Intelligence techniques specifically, Fuzzy Modeling and Neural Networks. The methodology at first, is implemented using the Matlab Fuzzy Logic Toolbox and with the help of a script the proces...

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Autores:
Palacios, Alejandro
Giraldo, Marcela
Quintero, O. L.
Tipo de recurso:
Fecha de publicación:
2014
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
eng
OAI Identifier:
oai:repository.eafit.edu.co:10784/4610
Acceso en línea:
http://hdl.handle.net/10784/4610
Palabra clave:
Energy Markets
Artificial Intelligence
Fuzzy Modeling
Neural Networks
Rights
License
Acceso abierto
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spelling 2014-12-12T15:41:27Z20142014-12-12T15:41:27Zhttp://hdl.handle.net/10784/4610This paper focuses on the study of an optimal portfolio in the Colombian Energy Market using the Artificial Intelligence techniques specifically, Fuzzy Modeling and Neural Networks. The methodology at first, is implemented using the Matlab Fuzzy Logic Toolbox and with the help of a script the process is automatized. Secondly, a Neural Network is implemented in Matlab and its results are compared with the ones obtained in the Matlab Neural Network Toolbox. The results of the Fuzzy model and the Neural Network are presented and conclusions of both techniques are discussed. Finally possible future work are proposed.engUniversidad EAFITGrupo de Investigación Modelado MatemáticoEscuela de CienciasColombian Energy Market: Optimal Portfolioinfo:eu-repo/semantics/workingPaperworkingPaperDocumento de trabajo de investigacióndrafthttp://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_8042Acceso abiertohttp://purl.org/coar/access_right/c_abf2Energy MarketsArtificial IntelligenceFuzzy ModelingNeural NetworksUniversidad EAFIT. Escuela de Ciencias. Grupo de Investigación Modelado MatemáticoPalacios, AlejandroGiraldo, MarcelaQuintero, O. L.ORIGINAL27 ColombianEnergyMarketOptimalPortfolio.pdf27 ColombianEnergyMarketOptimalPortfolio.pdfapplication/pdf695075https://repository.eafit.edu.co/bitstreams/4511ae6c-aac0-40af-bc70-69d85a9fe87c/download7f2555b83bb27d87987e7f1705203993MD5110784/4610oai:repository.eafit.edu.co:10784/46102014-12-12 13:48:08.332open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co
dc.title.eng.fl_str_mv Colombian Energy Market: Optimal Portfolio
title Colombian Energy Market: Optimal Portfolio
spellingShingle Colombian Energy Market: Optimal Portfolio
Energy Markets
Artificial Intelligence
Fuzzy Modeling
Neural Networks
title_short Colombian Energy Market: Optimal Portfolio
title_full Colombian Energy Market: Optimal Portfolio
title_fullStr Colombian Energy Market: Optimal Portfolio
title_full_unstemmed Colombian Energy Market: Optimal Portfolio
title_sort Colombian Energy Market: Optimal Portfolio
dc.creator.fl_str_mv Palacios, Alejandro
Giraldo, Marcela
Quintero, O. L.
dc.contributor.department.spa.fl_str_mv Universidad EAFIT. Escuela de Ciencias. Grupo de Investigación Modelado Matemático
dc.contributor.author.spa.fl_str_mv Palacios, Alejandro
Giraldo, Marcela
Quintero, O. L.
dc.subject.keyword.eng.fl_str_mv Energy Markets
Artificial Intelligence
Fuzzy Modeling
Neural Networks
topic Energy Markets
Artificial Intelligence
Fuzzy Modeling
Neural Networks
description This paper focuses on the study of an optimal portfolio in the Colombian Energy Market using the Artificial Intelligence techniques specifically, Fuzzy Modeling and Neural Networks. The methodology at first, is implemented using the Matlab Fuzzy Logic Toolbox and with the help of a script the process is automatized. Secondly, a Neural Network is implemented in Matlab and its results are compared with the ones obtained in the Matlab Neural Network Toolbox. The results of the Fuzzy model and the Neural Network are presented and conclusions of both techniques are discussed. Finally possible future work are proposed.
publishDate 2014
dc.date.available.none.fl_str_mv 2014-12-12T15:41:27Z
dc.date.issued.none.fl_str_mv 2014
dc.date.accessioned.none.fl_str_mv 2014-12-12T15:41:27Z
dc.type.none.fl_str_mv info:eu-repo/semantics/workingPaper
dc.type.eng.fl_str_mv workingPaper
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_8042
dc.type.local.spa.fl_str_mv Documento de trabajo de investigación
dc.type.hasVersion.eng.fl_str_mv draft
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10784/4610
url http://hdl.handle.net/10784/4610
dc.language.iso.eng.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.local.spa.fl_str_mv Acceso abierto
rights_invalid_str_mv Acceso abierto
http://purl.org/coar/access_right/c_abf2
dc.publisher.spa.fl_str_mv Universidad EAFIT
dc.publisher.program.spa.fl_str_mv Grupo de Investigación Modelado Matemático
dc.publisher.department.spa.fl_str_mv Escuela de Ciencias
institution Universidad EAFIT
bitstream.url.fl_str_mv https://repository.eafit.edu.co/bitstreams/4511ae6c-aac0-40af-bc70-69d85a9fe87c/download
bitstream.checksum.fl_str_mv 7f2555b83bb27d87987e7f1705203993
bitstream.checksumAlgorithm.fl_str_mv MD5
repository.name.fl_str_mv Repositorio Institucional Universidad EAFIT
repository.mail.fl_str_mv repositorio@eafit.edu.co
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