Modelling Electric Trains Energy Consumption Using Neural Networks

dc.contributor.affiliationDepartamento de Ingeniería e Infraestructura de los Transportes
dc.contributor.affiliationInstituto del Transporte y Territorio
dc.contributor.affiliationEscuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos
dc.contributor.authorMartínez Fernández, Pablo
dc.contributor.authorGarcía-Román, Carlaes_ES
dc.contributor.authorInsa Franco, Ricardo
dc.contributor.funderMinisterio de Ciencia e Innovaciónes_ES
dc.date.accessioned2018-06-04T04:25:12Z
dc.date.available2018-06-04T04:25:12Z
dc.date.issued2016es_ES
dc.description.abstract[EN] Nowadays there is an evident concern regarding the efficiency and sustainability of the transport sector due to both the threat of climate change and the current financial crisis. This concern explains the growth of railways over the last years as they present an inherent efficiency compared to other transport means. However, in order to further expand their role, it is necessary to optimise their energy consumption so as to increase their competitiveness. Improving railways energy efficiency requires both reliable data and modelling tools that will allow the study of different variables and alternatives. With this need in mind, this paper presents the development of consumption models based on neural networks that calculate the energy consumption of electric trains. These networks have been trained based on an extensive set of consumption data measured in line 1 of the Valencia Metro Network. Once trained, the neural networks provide a reliable estimation of the vehicles consumption along a specific route when fed with input data such as train speed, acceleration or track longitudinal slope. These networks represent a useful modelling tool that may allow a deeper study of railway lines in terms of energy expenditure with the objective of reducing the costs and environmental impact associated to railways.en_EN
dc.description.accrualMethodSes_ES
dc.description.bibliographicCitationMartínez Fernández, P.; García-Román, C.; Insa Franco, R. (2016). Modelling Electric Trains Energy Consumption Using Neural Networks. Transportation Research Procedia. 18:59-65. https://doi.org/10.1016/j.trpro.2016.12.008es_ES
dc.description.sponsorshipThe authors wish to thank Ferrocarrils de la Generalitat Valenciana (FGV) for their permission and help during the monitoring campaign. Project funded by the Spanish Ministry of Economy and Competitiveness (Grant Number TRA2011-26602).
dc.description.upvformatpfin65es_ES
dc.description.upvformatpinicio59es_ES
dc.description.volume18es_ES
dc.identifier.doi10.1016/j.trpro.2016.12.008es_ES
dc.identifier.eissn2352-1465es_ES
dc.identifier.urihttps://riunet.upv.es/handle/10251/103303
dc.languageIngléses_ES
dc.publisherElsevieres_ES
dc.relation.conferencedateJune 07-09,2016es_ES
dc.relation.conferencenameXII Congreso de Ingeniería del Transporte (CIT 2016)es_ES
dc.relation.conferenceplaceValencia, Spaines_ES
dc.relation.ispartofTransportation Research Procediaes_ES
dc.relation.pasarelaS\354134es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN//TRA2011-26602/ES/ESTRATEGIAS PARA EL DISEÑO Y LA EXPLOTACION ENERGETICAMENTE EFICIENTE DE INFRAESTRUCTURAS FERROVIARAS Y TRANVIARIAS/es_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.trpro.2016.12.008es_ES
dc.rightsReconocimiento - No comercial - Sin obra derivada (by-nc-nd)es_ES
dc.rights.accessRightsAbiertoes_ES
dc.subjectRailwayses_ES
dc.subjectNeural Networkses_ES
dc.subjectEnergy efficiencyes_ES
dc.subjectModellinges_ES
dc.subjectMetro.es_ES
dc.subject.classificationINGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTESes_ES
dc.titleModelling Electric Trains Energy Consumption Using Neural Networkses_ES
dc.typeArtículoes_ES
dc.typeComunicación en congresoes_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dspace.entity.typePublication
person.identifier292360
person.identifier747
person.identifier.orcid0000-0002-8246-2510
person.identifier.orcid0000-0002-6655-4458
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relation.isAuthorOfPublication.latestForDiscovery59a27af9-4249-452d-b28c-93292c10a0fc
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upv.uuid60d6b0f2-30fa-408c-b0c8-565a88d2cf5ces_ES

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