RT info:eu-repo/semantics/article
T1 Analysis of parallel process in HVAC systems using deep autoencoders
A1 Morán Palao, Antonio
A1 Alonso Castro, Serafín
A1 Prada Medrano, Miguel Ángel
A1 Fuertes Martínez, Juan José
A1 Díaz, Ignacio
A1 Domínguez, Manuel
A2 Ingenieria de Sistemas y Automatica
K1 Ingeniería industrial
K1 Dimensionality reduction
K1 Information visualization
K1 Data analysis
K1 Deep autoencoder
K1 HVAC systems
AB 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
PB Springer
YR 2018
FD 2018-03-05
LK http://hdl.handle.net/10612/7450
UL http://hdl.handle.net/10612/7450
NO International Conference on Engineering Applications of Neural Networks, 2017
NO P. 15-26
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 04-may-2024