A practical approach to chiller plants' optimisation

被引:39
作者
Wang, Lan [1 ]
Lee, Eric W. M. [1 ]
Yuen, Richard K. K. [1 ]
机构
[1] City Univ Hong Kong, Dept Architecture & Civil Engn, Tat Chee Ave, Kowloon Tong, Hong Kong, Peoples R China
关键词
HVAC SYSTEM; GENETIC ALGORITHM; NEURAL-NETWORKS; SIMULATION; VALIDATION;
D O I
10.1016/j.enbuild.2018.03.076
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Chiller plants use more than 50% of the total energy consumed by heating ventilation and air conditioning (HVAC) systems. Adjustments to the interactions between components could reduce the total power used, in the premise of helping systems to meet building thermal load targets. Studies have shown that significant power reduction can be achieved by adopting different kinds of optimisation techniques to different variables. However, it is difficult to apply these optimisation strategies into industry, as frequent simultaneous updates of different variables with large fluctuations harm the operational stability of chiller plants. In practice, making fewer and more moderate variable updates is a more practical optimisation strategy. To address this problem, this study proposes using varying searching bounds in the optimisation processes. To determine the contribution of each variable to power reduction, the effect of each optimised variable is analysed. The results show that applying varying searching bounds is an effective way to avoid frequent large fluctuations in optimised variables without sacrificing much optimisation performance. The optimisations of the condenser water mass flow rate and condenser water supplying temperature make the greater contributions to power reduction. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:332 / 343
页数:12
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