建筑空调系统最优停机时间预测与控制

徐小艳 1; 管晓宏 1; 李黎 2

doi:10.7652/xjtuxb201310006

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建筑空调系统最优停机时间预测与控制

更新时间：2025-07-09

- 建筑空调系统最优停机时间预测与控制
- Predicting the Optimal Shut-Down Time of Heat, Ventilation, Air-Conditioning and Cooling System in Building
- 西安交通大学学报 2013年47卷第10期页码：31-36+61
- 作者机构：
  
  西安交通大学智能网络与网络安全教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2012-12-14。作者简介: 徐小艳(1981—),女,博士生;管晓宏(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(60921003);国家“863计划”资助项目(2007AA04Z154);中央高校基本科研业务费专项资金资助项目(GK201102008)
- DOI：10.7652/xjtuxb201310006
  中图分类号： TP273
- 网络首发：2013-10-10，
  
  纸质出版：2013
- 稿件说明：
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徐小艳 1, 管晓宏 1, 李黎 2. 建筑空调系统最优停机时间预测与控制[J]. 西安交通大学学报, 2013,47(10):31-36+61.

Predicting the Optimal Shut-Down Time of Heat, Ventilation, Air-Conditioning and Cooling System in Building[J]. 2013, 47(10): 31-36+61.
徐小艳 1, 管晓宏 1, 李黎 2. 建筑空调系统最优停机时间预测与控制[J]. 西安交通大学学报, 2013,47(10):31-36+61. DOI： 10.7652/xjtuxb201310006.

Predicting the Optimal Shut-Down Time of Heat, Ventilation, Air-Conditioning and Cooling System in Building[J]. 2013, 47(10): 31-36+61. DOI： 10.7652/xjtuxb201310006.

摘要

针对建筑空调系统在停机阶段联合优化控制自然通风和室内空调系统

以在满足室内人员舒适的同时尽量减小建筑总能耗的问题

研究了自然通风量、风机盘管和新风送风量为控制输入

室内环境

包括空气温度、空气含湿量为系统状态

建筑总能耗为目标

室内人员舒适度为约束

且控制变量和状态变量受约束的最优控制问题。研究中通过采用神经网络来逼近和预测天气、室内初始环境参数与空调最优停机时间、最优联合控制策略之间的非线性映射关系

找到了一条简单可行、性能较好的非最优策略

极大地提高了联合控制策略的普适性和推广能力; 经神经网络结构参数的优化配置后

建筑空调系统停机时间预测和控制的可靠性和精准性有所提高。室内环境状态和建筑能耗的数值比较表明:所提策略在有效降低问题复杂度和计算成本的同时

仿真时间在1 h的尺度上可进一步节能约20%。

Abstract

The problem of optimal joint control of heating

ventilation

air-conditioning and cooling(HVAC)as well as natural ventilation during the HAVC shut-down stage is studied for energy saving while maintaining indoor thermal comfort. The problem is formulated as a constrained optimization problem by taking the volume of natural ventilation and the statuses of the supplied air of both FCU and FAU as system inputs

the indoor air temperature and the indoor air moisture content as system states

the total building energy consumption as the optimization objective

and the comfort requirements of indoor personnel as constraints. Results show that a good-enough policy instead of the best policy can be obtained by using ANN to approximate and to predict the relationship among the climate parameters

the initial conditions of indoor environment

the optimal shut-down time of HVAC

and the joint control policy of natural ventilation and HVAC. The policy has higher adaptability and generalization ability

and the predicting accuracy and reliability of ANN can be further improved by optimizing its configuration. Comparisons of both the indoor environment and the building energy consumption show that the good-enough policy further achieves 20% reduction in building energy consumption in one hour simulation.

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references

MCQUADE J M. A system approach to high performance buildings [EB/OL]. [2012-12-10]. http:∥gop.science.house.gov/media/hearing/energy09/april28/mcquade.pdf.

SHELTON S V, JOYCE C T. Cooling tower optimization for centrifugal chillers [J]. ASHRAE Journal, 1991, 36(6): 28-36.

BRAUN J E, KLEIN S A, MITCHEL J A, et al. Application of optimal control to chilled water system without storage [J]. ASHRAE Transaction, 1989, 95(1): 663-675.

HOUSE J M, SMITH T F. A system approach to optimal control HVAC and buildings [J]. ASHRAE Transaction, 1995, 101(2): 647-640.

BRAUN J E, ZHONG Zhipeng. Development and evaluation of a night ventilation and precooling algorithm [J]. HVACR Research, 2005, 11(3): 438-458.

XU Xiaoyan, JIA Qingshan, GUAN Xiaohong, et al. Energy saving potential using natural ventilation and shading in four major cities in China [C]∥The 30th Chinese Conferences on Control. Beijing, China: Chinese Academy of Sciences, 2011: 742-748.

YAO Runming, LI Baizhan, STEEMERS K, et al. Assessing the natural ventilation cooling potential of office building in different climate zones in China [J]. Renewable Energy Building and Environment, 2009, 34(1): 2697-2705.

ZHOU Junli, ZHANG Guoqiang, LIN Yaolin, et al. Coupling of thermal mass and natural ventilation in buildings [J]. Energy and Buildings, 2008, 40(6): 979-986.

HOES P, HENSEN J L M, LOOMANS M G L C, et al. User behavior in whole building simulation [J]. Energy and Buildings, 2009, 41(3): 295-302.

YANG I H, YEO M S, KIM K W. Application of artificial neural network to predict the optimal start time for heating system in building [J]. Energy Conversion and Management, 2003, 44(17): 2791-2809.

YANG I H, KIM K W. Prediction of the time of room air temperature descending for heating systems in buildings [J]. Building and Environment, 2004, 39(1): 19-29.

HUANG W Z, ZAHEER-UDDIN M, CHAO S H. Dynamic simulation of the energy management control functions for HVAC system in buildings [J]. Energy Conversion and Management, 2006, 47(6): 926-943.

CURTISS P S. Artificial neural network for use in building systems control and energy management [D]. Colorado, Canada: University of Colorado, 1992.

徐小艳, 贾庆山, 管晓宏. 基于仿真的建筑空调和自然通风联合策略优化 [C]∥第二届中国自动控制会议. 北京: 中国自动化学会, 2011: 137-145.

江亿, 燕达, 宋芳婷. 建筑能耗模拟方法与技术 [M]. 北京: 中国建筑工业出版社, 2004.

邓伟, 金丕焕. 人工神经网络及其在预防医学中的应用 [J]. 中国公共卫生, 2002, 18(10): 1265-1267.

DENG Wei, JIN Pihuan. Artificial neural network and its applications in preventive medical system [J]. Journal of Chinese Public Health, 2002, 18(10): 1265-1267.

ZHANG Beibei, GUAN Xiaohong, KHAN M J, et al. A time-varying propagation model of hot topic on BBS sites and Blog networks [J]. Information Sciences, 2012, 187(15): 15-32.

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