Analysis of parallel process in HVAC systems using deep autoencoders

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Analysis of parallel process in HVAC systems using deep autoencoders

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Title: Analysis of parallel process in HVAC systems using deep autoencoders
Author: Morán, Antonio;Alonso, Serafín;Prada, Miguel A.;Fuertes, Juan J.;Díaz, Ignacio;Domínguez, Manuel
xmlui.dri2xhtml.METS-1.0.item-contributor: Escuela de Ingenierias Industrial e Informatica
xmlui.dri2xhtml.METS-1.0.item-area: Ingenieria de Sistemas y Automatica
Abstract: Heating, 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 León
xmlui.dri2xhtml.METS-1.0.item-desfisica: P. 15-26
xmlui.dri2xhtml.METS-1.0.item-peerreviewed: SI
Publisher: Springer
xmlui.dri2xhtml.METS-1.0.item-citation: International Conference on Engineering Applications of Neural Networks, 2017
Date: 2017-10-23
xmlui.dri2xhtml.METS-1.0.item-tipo: info:eu-repo/semantics/article
Subject: Ingeniería industrial
xmlui.dri2xhtml.METS-1.0.item-palclave: Dimensionality reduction
Information visualization
Data analysis
Deep autoencoder
HVAC systems
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