Full Text:   <1753>

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CLC number: TK323; TP29

On-line Access: 2015-04-03

Received: 2014-05-11

Revision Accepted: 2014-10-29

Crosschecked: 2015-03-23

Cited: 0

Clicked: 2826

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Qing-long Meng

http://orcid.org/0000-0002-3976-2331

Xiu-ying Yan

http://orcid.org/0000-0001-9949-8870

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Journal of Zhejiang University SCIENCE A 2015 Vol.16 No.4 P.302-315

http://doi.org/10.1631/jzus.A1400137


Global optimal control of variable air volume air-conditioning system with iterative learning: an experimental case study


Author(s):  Qing-long Meng, Xiu-ying Yan, Qing-chang Ren

Affiliation(s):  School of Environmental Science and Engineering, Chang’an University, Xi’an 710054, China; more

Corresponding email(s):   mql19@163.com, xjdyxy1219@163.com

Key Words:  Air-conditioning system, Large scale systems, Iterative learning control (ILC), Global optimization


Qing-long Meng, Xiu-ying Yan, Qing-chang Ren. Global optimal control of variable air volume air-conditioning system with iterative learning: an experimental case study[J]. Journal of Zhejiang University Science A, 2015, 16(4): 302-315.

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%A Xiu-ying Yan
%A Qing-chang Ren
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Abstract: 
The air-conditioning system in a large commercial or high-rise building is a complex multi-variable system influenced by many factors. The energy saving potential from the optimal operation and control of heating, ventilating, and air-conditioning (HVAC) systems can be large, even when they are properly designed. The ultimate goal of optimization is to use the minimum amount of energy needed to improve system efficiency while meeting comfort requirements. In this study, a multi-zone variable air volume (VAV) and variable water volume (VWV) air-conditioning system is developed. The steady state modes and dynamic models of the HVAC subsystems are constructed. Optimal control based on large scale system theory for system-level energy-saving of HVAC is introduced. Control strategies such as proportional-integral-derivative (PID) controller (gearshift integral PID and self-tuning PID) and iterative learning control (ILC) are studied in the platform to improve the dynamic characteristics. The system performance is improved. An 18.2% energy saving is achieved with the integration of ILC and sequential quadratic programming based on a steady-state hierarchical optimization control scheme.

The authors provide an interesting paper in the area of HVAC system; present a novel scheme for HVAC control. The authors developed a HVAC system based on a hierarchical optimization control scheme and dynamic HVAC subsystems models to minimize energy consumption. The general idea of the study is very good. The algorithm was reported to be efficient and realize 18.2% energy saving based on experimental results.

基于迭代学习的变风量空调系统全局优化控制实验研究

目的:采用实验方法研究变风量全局优化问题,利用迭代学习控制策略优化动态控制性能,获得变风量系统在系统层次的最优。
创新点:1. 采用全新的兼有变风量和变水量功能的实验平台;2. 引入递阶优化控制理论,建立变风量系统的动态和稳态模型;3. 采用先进控制策略,如自校正比例积分微分(PID)控制和迭代学习控制等。
方法:1. 将系统进行分解(图4),并建立系统稳态模型(公式6-11)、动态模型(公式12-15)和能耗模型(公式16);2. 在此基础上采用变速积分PID、自校正PID和迭代学习控制对系统底层进行动态控制,在系统整体优化中引入迭代学习。
结论:1. 先进控制策略的引入有利于优化变风量系统动态控制过程;2. 采用基于迭代学习的优化方法,可使系统节能约18.2%。

关键词:空调;大系统;迭代学习控制;全局优化

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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