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dc.contributorEscuela de Ingenierias Industrial e Informaticaes_ES
dc.contributor.authorMorán, Antonio
dc.contributor.authorAlonso, Serafín
dc.contributor.authorPrada Medrano, Miguel Angel
dc.contributor.authorFuertes Martínez, Juan José 
dc.contributor.authorDíaz, Ignacio
dc.contributor.authorDomínguez, Manuel
dc.contributor.otherIngenieria de Sistemas y Automaticaes_ES
dc.date2017-10-23
dc.date.accessioned2018-03-05T13:17:45Z
dc.date.available2018-03-05T13:17:45Z
dc.date.issued2018-03-05
dc.identifier.citationInternational Conference on Engineering Applications of Neural Networks, 2017es_ES
dc.identifier.urihttp://hdl.handle.net/10612/7450
dc.descriptionP. 15-26es_ES
dc.description.abstractHeating, Ventilation, and Air Conditioning (HVAC) systems are generally built in a modular manner, comprising several identical subsystems in order to achieve their nominal capacity. These parallel subsystems and elements should have the same behavior and, therefore, differences between them can reveal failures and inefficiency in the system. The complexity in HVAC systems comes from the number of variables involved in these processes. For that reason, dimensionality reduction techniques can be a useful approach to reduce the complexity of the HVAC data and study their operation. However, for most of these techniques, it is not possible to project new data without retraining the projection and, as a result, it is not possible to easily compare several projections. In this paper, a method based on deep autoencoders is used to create a reference model with a HVAC system and new data is projected using this model to be able to compare them. The proposed approach is applied to real data from a chiller with 3 identical compressors at the Hospital of Leónes_ES
dc.languageenges_ES
dc.publisherSpringeres_ES
dc.subjectIngeniería industriales_ES
dc.subject.otherDimensionality reductiones_ES
dc.subject.otherInformation visualizationes_ES
dc.subject.otherData analysises_ES
dc.subject.otherDeep autoencoderes_ES
dc.subject.otherHVAC systemses_ES
dc.titleAnalysis of parallel process in HVAC systems using deep autoencoderses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.peerreviewedSIes_ES


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