Analysis of parallel process in HVAC systems using deep autoencoders

Repositorio Dspace/Manakin

Analysis of parallel process in HVAC systems using deep autoencoders

Mostrar el registro completo del ítem

Título: Analysis of parallel process in HVAC systems using deep autoencoders
Autor: Morán, Antonio;Alonso, Serafín;Prada, Miguel A.;Fuertes, Juan J.;Díaz, Ignacio;Domínguez, Manuel
Facultad/Centro: Escuela de Ingenierias Industrial e Informatica
Area de conocimiento: Ingenieria de Sistemas y Automatica
Resumen: 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
Descripción física: P. 15-26
Revisión por pares: SI
Editor: Springer
Datos: International Conference on Engineering Applications of Neural Networks, 2017
URI: http://hdl.handle.net/10612/7450
Fecha: 2017-10-23
Tipo: info:eu-repo/semantics/article
Materia: Ingeniería industrial
Palabras clave: Dimensionality reduction
Information visualization
Data analysis
Deep autoencoder
HVAC systems
Exportar referencia a Refworks:


Ficheros en el ítem

Ficheros Tamaño Formato Ver
moran17_preprint.pdf 2.862Mb PDF Ver/Abrir

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem

Buscar en BULERIA


Búsqueda avanzada

Listar

Mi cuenta

Estadísticas