Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31740
Title: Neural network based prediction method for preventing condensation in chilled ceiling systems
Authors: Ge, G
Xiao, F 
Wang, S 
Keywords: Chilled ceiling
Condensation prevention
Dedicated outdoor air system
Neural network
Issue Date: 2012
Publisher: Elsevier
Source: Energy and buildings, 2012, v. 45, p. 290-298 How to cite?
Journal: Energy and buildings 
Abstract: Condensation is prone to occur at the startup moment in chilled ceiling systems, due to the infiltration and accumulation of moisture during system-off. To prevent condensation, an effective method is to operate the dedicated outdoor air system (DOAS) to dehumidify indoor air before operating chilled ceiling system. The pre-dehumidification time is critical. However, there is little experience in determining the pre-dehumidification time in both research and practice. In this study, neural network (NN) is used to predict condensation risk and the optimal pre-dehumidification time in chilled ceiling systems. Two NN models are developed to predict the temperature on the surface of chilled ceiling and indoor air dew-point temperature at the startup moment so as to evaluate the risk of condensation. The third NN model is developed to predict the optimal pre-humidification time for condensation prevention. Both training data and validation data are obtained from simulation tests in TRNSYS. The results show that 30 min pre-dehumidification is sufficient for the simulated building in Hong Kong. The influence of infiltration rate on the pre-dehumidification time is also investigated. This study also shows that NN-based method can be used for predictive control for condensation prevention in chilled ceiling systems.
URI: http://hdl.handle.net/10397/31740
ISSN: 0378-7788
EISSN: 1872-6178
DOI: 10.1016/j.enbuild.2011.11.017
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

15
Last Week
0
Last month
1
Citations as of Aug 13, 2017

WEB OF SCIENCETM
Citations

15
Last Week
0
Last month
0
Citations as of Aug 14, 2017

Page view(s)

33
Last Week
2
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.