Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30387
Title: Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network
Authors: Li, N
Xia, L
Deng, S 
Xu, X
Chan, MY 
Keywords: Air conditioning
Artificial neural network
Control
Direct expansion
Dynamic modeling
Variable speed
Issue Date: 2012
Publisher: Pergamon Press
Source: Applied energy, 2012, v. 91, no. 1, p. 290-300 How to cite?
Journal: Applied energy 
Abstract: An artificial neural network (ANN)-based dynamic model for an experimental variable speed direct expansion (DX) air conditioning (A/C) system has been developed, linking the indoor air temperature and humidity controlled by the DX A/C system with the variations of compressor and supply fan speeds. The values of average relative error (ARE) and maximum relative error (MRE) when validating the ANN-based dynamic model developed under three different input patterns were 0.33%, 0.27%, 0.27% and 0.89%, 0.99%, 1.15%, respectively, indicating the high accuracy of the ANN-based dynamic model developed. An ANN-based controller was then developed for controlling the indoor air temperature and humidity simultaneously by varying the compressor speed and supply fan speed in a space served by the experimental DX A/C system. The controllability tests including command following test and disturbance rejection test were carried out using the experimental DX A/C system, and the test results showed that the ANN-based controller developed was able to track the changes in setpoints and to resist the disturbances.
URI: http://hdl.handle.net/10397/30387
ISSN: 0306-2619
EISSN: 1872-9118
DOI: 10.1016/j.apenergy.2011.09.037
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