Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/44138
Title: Hybrid artificial neural network-genetic algorithm technique for condensing temperature control of air-cooled chillers
Authors: Yang, J 
Chan, KT 
Dai, T
Yu, FW 
Chen, L
Keywords: Air-cooled chiller
Artificial neural network
Condensing temperature control
Genetic algorithm
Issue Date: 2015
Publisher: Elsevier
Source: Procedia engineering, 2015, v. 121, p. 706-713 How to cite?
Journal: Procedia engineering 
Abstract: Air-cooled chillers are commonly used in commercial buildings in the subtropical climate, which are considered inefficient due to operating under traditional head pressure control. This study presents a hybrid intelligent control technique, including neural networks and genetic algorithms, for the optimal control of the set points of the condensing temperature to improve the coefficient of performance (COP) of air-cooled chillers under various operating conditions. The neural network is used to model the air-cooled chillers, and genetic algorithm is adopted in searching optimal set points of condensing temperature based on the predicted fitness values. Results show that this control technique allows optimal set point of the condensing temperature to be successfully determined, and the chiller performance can be improved considerably.
Description: 9th International Symposium on Heating, Ventilation and Air Conditioning, ISHVAC 2015 Joint with the 3rd International Conference on Building Energy and Environment, COBEE 2015, 12-15 July 2015
URI: http://hdl.handle.net/10397/44138
ISSN: 1877-7058
DOI: 10.1016/j.proeng.2015.09.012
Rights: © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The following publication Yang, J., Chan, K. T., Dai, T., Yu, F. W., & Chen, L. (2015). Hybrid Artificial Neural Network− Genetic Algorithm Technique for Condensing Temperature Control of Air-Cooled Chillers. Procedia engineering, 121, 706-713. is available at https://doi.org/10.1016/j.proeng.2015.09.012
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