Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain)

This paper presents the training of an artificial neural network using consumption data measured in the metropolitan network of Valencia, Spain, to estimate the energy consumption of a metro system. After calibration and validation of the neural network, the results obtained show that it can be used...

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Autores:: Pineda-Jaramillo, Juan Diego
Salvador-Zuriaga, Pablo
Insa-Franco, Ricardo

Tipo de recurso:: Article of journal

Fecha de publicación:: 2017

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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spelling	Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Pineda-Jaramillo, Juan Diegod1da8b0d-cf1e-44cd-bdbb-d0539fd75ed1300Salvador-Zuriaga, Pablo5b74963b-44b1-41dd-b52a-52a1f62bd09b300Insa-Franco, Ricardoa2714a0f-f3dc-429b-8114-3825f47e16003002019-07-02T19:18:40Z2019-07-02T19:18:40Z2017-10-01ISSN: 2346-2183https://repositorio.unal.edu.co/handle/unal/60865http://bdigital.unal.edu.co/59247/This paper presents the training of an artificial neural network using consumption data measured in the metropolitan network of Valencia, Spain, to estimate the energy consumption of a metro system. After calibration and validation of the neural network, the results obtained show that it can be used to predict energy consumption with high accuracy. Once fully trained, the neural network is used for testing hypothetical operational scenarios aimed to reduce the energy consumption of a metro system. These operational scenarios include different vertical alignments that prove that Symmetric Vertical Sinusoid Alignments (SVSA) can reduce energy consumption by 18.41% in contrast to a flat (0% gradient) alignment.Este artículo presenta el entrenamiento de una red neuronal artificial usando el consumo energético medido en la red metropolitana de Valencia, España, para estimar el consumo energético de un sistema metro. Después de la calibración y validación de la red neuronal, los resultados obtenidos muestran que esta puede ser utilizada para predecir el consumo energético con una gran precisión. Una vez entrenada, la red neuronal es utilizada para probar diferentes escenarios de operación hipotéticos con el objetivo de reducir el consumo energético de un sistema metro. Estos escenarios de operación incluyen diferentes trazados verticales que prueban que los Alineamientos Verticales Sinusoidales Simétricos (SVSA, por sus siglas en inglés) pueden reducir el consumo energético en un 18.41 % en contraste con un alineamiento plano (pendiente del 0%).application/pdfspaUniversidad Nacional de Colombia (Sede Medellín). Facultad de Minas.https://revistas.unal.edu.co/index.php/dyna/article/view/65267Universidad Nacional de Colombia Revistas electrónicas UN DynaDynaPineda-Jaramillo, Juan Diego and Salvador-Zuriaga, Pablo and Insa-Franco, Ricardo (2017) Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain). DYNA, 84 (203). pp. 17-23. ISSN 2346-218362 Ingeniería y operaciones afines / EngineeringSymmetric Vertical Sinusoid Alignments (SVSA)gradientenergy consumptionartificial neural networksmetro systemAlineamientos Verticales Sinusoidales Simétricos (SVSA, por sus siglas en inglés)pendienteconsumo energéticoredes neuronales artificialessistema metroComparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain)Artículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL65267-356389-1-PB.pdfapplication/pdf550184https://repositorio.unal.edu.co/bitstream/unal/60865/1/65267-356389-1-PB.pdfb857b88390c39b1fa1bf7fe879dfe2cbMD51THUMBNAIL65267-356389-1-PB.pdf.jpg65267-356389-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg10139https://repositorio.unal.edu.co/bitstream/unal/60865/2/65267-356389-1-PB.pdf.jpge31dd2694fcba9776491aa3b2576ac63MD52unal/60865oai:repositorio.unal.edu.co:unal/608652023-04-09 23:05:07.439Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co
dc.title.spa.fl_str_mv	Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain)
title	Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain)
spellingShingle	Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain) 62 Ingeniería y operaciones afines / Engineering Symmetric Vertical Sinusoid Alignments (SVSA) gradient energy consumption artificial neural networks metro system Alineamientos Verticales Sinusoidales Simétricos (SVSA, por sus siglas en inglés) pendiente consumo energético redes neuronales artificiales sistema metro
title_short	Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain)
title_full	Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain)
title_fullStr	Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain)
title_full_unstemmed	Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain)
title_sort	Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain)
dc.creator.fl_str_mv	Pineda-Jaramillo, Juan Diego Salvador-Zuriaga, Pablo Insa-Franco, Ricardo
dc.contributor.author.spa.fl_str_mv	Pineda-Jaramillo, Juan Diego Salvador-Zuriaga, Pablo Insa-Franco, Ricardo
dc.subject.ddc.spa.fl_str_mv	62 Ingeniería y operaciones afines / Engineering
topic	62 Ingeniería y operaciones afines / Engineering Symmetric Vertical Sinusoid Alignments (SVSA) gradient energy consumption artificial neural networks metro system Alineamientos Verticales Sinusoidales Simétricos (SVSA, por sus siglas en inglés) pendiente consumo energético redes neuronales artificiales sistema metro
dc.subject.proposal.spa.fl_str_mv	Symmetric Vertical Sinusoid Alignments (SVSA) gradient energy consumption artificial neural networks metro system Alineamientos Verticales Sinusoidales Simétricos (SVSA, por sus siglas en inglés) pendiente consumo energético redes neuronales artificiales sistema metro
description	This paper presents the training of an artificial neural network using consumption data measured in the metropolitan network of Valencia, Spain, to estimate the energy consumption of a metro system. After calibration and validation of the neural network, the results obtained show that it can be used to predict energy consumption with high accuracy. Once fully trained, the neural network is used for testing hypothetical operational scenarios aimed to reduce the energy consumption of a metro system. These operational scenarios include different vertical alignments that prove that Symmetric Vertical Sinusoid Alignments (SVSA) can reduce energy consumption by 18.41% in contrast to a flat (0% gradient) alignment.
publishDate	2017
dc.date.issued.spa.fl_str_mv	2017-10-01
dc.date.accessioned.spa.fl_str_mv	2019-07-02T19:18:40Z
dc.date.available.spa.fl_str_mv	2019-07-02T19:18:40Z
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.issn.spa.fl_str_mv	ISSN: 2346-2183
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/60865
dc.identifier.eprints.spa.fl_str_mv	http://bdigital.unal.edu.co/59247/
identifier_str_mv	ISSN: 2346-2183
url	https://repositorio.unal.edu.co/handle/unal/60865 http://bdigital.unal.edu.co/59247/
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.spa.fl_str_mv	https://revistas.unal.edu.co/index.php/dyna/article/view/65267
dc.relation.ispartof.spa.fl_str_mv	Universidad Nacional de Colombia Revistas electrónicas UN Dyna Dyna
dc.relation.references.spa.fl_str_mv	Pineda-Jaramillo, Juan Diego and Salvador-Zuriaga, Pablo and Insa-Franco, Ricardo (2017) Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain). DYNA, 84 (203). pp. 17-23. ISSN 2346-2183
dc.rights.spa.fl_str_mv	Derechos reservados - Universidad Nacional de Colombia
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
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rights_invalid_str_mv	Atribución-NoComercial 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia (Sede Medellín). Facultad de Minas.
institution	Universidad Nacional de Colombia
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Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain)

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