变风量空调系统递阶结构协调优化控制研究
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摘要
变风量中央空调系统作为智能建筑的重要组成部分，凭借其舒适、节能、灵活性等方面的优势被广泛关注。而现场工程及控制过程中系统的运行由于受到建筑物环境及空调负荷变化等因素的影响，使得各设备的运行偏离了所设计的最优工作点，从而影响系统整体的运行和节能。如何从系统全局出发，根据现场运行情况，构建系统稳态模型，搜索全局最优设定点，在满足室内空气品质和舒适性的前提下，最大程度地降低系统能耗，成为一个有意义的研究课题。
对于中央空调这个由多个子系统组成的大系统，在局部控制优化的基础上，实现全局系统优化控制，将局部控制环节整合起来，从大系统的角度综合决策、协调优化是一种行之有效的手段。论文针对节能环保、室内空气品质及人体舒适度的综合需求，寻求系统全局层面的解决方案。
论文基于大系统“分解-协调”理论与优化控制策略，在对智能建筑变风量(VAV)中央空调系统进行递阶结构分解的基础上，介绍实验系统的整体软硬件平台及各子系统的功能设计。
分别从控制算法和节能策略两个角度进行研究。建立各子系统动态模型，并设计广义预测控制器及神经网络PID控制器用于底层子系统的控制。实现了基于广义预测控制算法的风系统变静压控制策略及基于NN-PID控制算法的需求控制通风策略。实验表明，算法具有较强跟踪性及抗干扰能力的同时体现出可观的节能潜力。
引入室外气象参数用于空调负荷的预测，提出一种修正的ASHRAE系数法预测室外逐时温度，由此构建训练数据集。设计Elman神经网络及Grey-NN神经网络预测算法，对空调系统动态负荷的预测结果为全局优化目标函数及约束的确定提供依据。
研究变风量空调大系统的稳态优化问题，建立系统稳态模型；构建全局系统优化运行工况模型；设计改进的关联平衡法(IBM)对全局系统进行协调优化，可在最优点处使关联达到平衡，且保证协调的收敛性；在设定的优化周期内，通过两级之间的交互，寻得各子系统的优化设定值，进而送至底层控制器，完成递阶优化控制。
以冬季工况为例进行的实验结果表明，采用全局优化策略能够很好解决中央空调全局系统的控制和优化问题，具有一定的节能潜力，且可推广到一类大范围工况过程系统。
As an important part of the intelligent building, VAV central air conditioningsystem is of widespread concern due to its advantages of comfort, energy saving andflexibility. But in the engineering and controlling process, the actual operation of eachdevice may deviate from the optimal design, thus affecting the operation and energyefficiency of the overall system. Therefore, it’s of high significance to constructsteady-state model, search global optimal set point according to the actual situation, andminimize the energy consumption on the basis of indoor air quality and human comfort.
For central air conditioning system composed of multiple subsystems, it is aneffective means to make integrated decision from the large-scale systems based on localcontrol optimization. The thesis focuses on the comprehensive needs of energy saving,indoor air quality and human comfort, to explore the overall solution to the system.
In the research, the VAV air conditioning system is decomposed in hierarchicalstructure on the basis of large-scale system decomposition-coordination theory andoptimal control strategy, and the overall hardware and software platform of theexperimental system and the design of its subsystem function are introduced.
The research was conducted from the perspectives of energy-saving strategies andcontrol algorithms. The subsystem dynamic model was constructed and the GPCalgorithm and Neural Network-PID control algorithm for local control unit wasdesigned. Herein, variable static pressure control strategy and demand controlventilation strategy were implemented based on the generalized predictive controlalgorithm and NN-PID control algorithm respectively. The experiments show that the algorithm with strong tracking and anti-jamming capability exhibits the considerablepotential for energy saving.
With the outdoor meteorological parameters adopted to predict the air-conditioningload, ASHRAE correction coefficient method was proposed to predict the outdoorhourly temperature. Thereby the training data set was constructed. The Elman neuralnetwork and Grey-NN neural network prediction algorithm were designed, and theforecast results of air conditioning system dynamic load could be the basis for theobjective function and constraints of the global optimization.
Steady-state optimization problem of VAV air conditioning system was studied,and the steady state model was established in the research. The global systemoptimization operating conditions model was constructed. An improved interbalancemethod (IBM) was designed to coordinate the global system, and the association couldbe equilibrium in the optimal point to ensure the convergence of coordination.Energy-saving optimization of the system control was implemented according to thesearched optimized value in each optimization cycle through the interaction between thetwo levels.
The experimental results show that, using global optimization strategy can be agood solution to the central air-conditioning control and optimization of the globalsystem with large energy saving potential in winter conditions. And the strategy can beextended to a class of large-scale conditions process systems.
对于中央空调这个由多个子系统组成的大系统，在局部控制优化的基础上，实现全局系统优化控制，将局部控制环节整合起来，从大系统的角度综合决策、协调优化是一种行之有效的手段。论文针对节能环保、室内空气品质及人体舒适度的综合需求，寻求系统全局层面的解决方案。
论文基于大系统“分解-协调”理论与优化控制策略，在对智能建筑变风量(VAV)中央空调系统进行递阶结构分解的基础上，介绍实验系统的整体软硬件平台及各子系统的功能设计。
分别从控制算法和节能策略两个角度进行研究。建立各子系统动态模型，并设计广义预测控制器及神经网络PID控制器用于底层子系统的控制。实现了基于广义预测控制算法的风系统变静压控制策略及基于NN-PID控制算法的需求控制通风策略。实验表明，算法具有较强跟踪性及抗干扰能力的同时体现出可观的节能潜力。
引入室外气象参数用于空调负荷的预测，提出一种修正的ASHRAE系数法预测室外逐时温度，由此构建训练数据集。设计Elman神经网络及Grey-NN神经网络预测算法，对空调系统动态负荷的预测结果为全局优化目标函数及约束的确定提供依据。
研究变风量空调大系统的稳态优化问题，建立系统稳态模型；构建全局系统优化运行工况模型；设计改进的关联平衡法(IBM)对全局系统进行协调优化，可在最优点处使关联达到平衡，且保证协调的收敛性；在设定的优化周期内，通过两级之间的交互，寻得各子系统的优化设定值，进而送至底层控制器，完成递阶优化控制。
以冬季工况为例进行的实验结果表明，采用全局优化策略能够很好解决中央空调全局系统的控制和优化问题，具有一定的节能潜力，且可推广到一类大范围工况过程系统。
As an important part of the intelligent building, VAV central air conditioningsystem is of widespread concern due to its advantages of comfort, energy saving andflexibility. But in the engineering and controlling process, the actual operation of eachdevice may deviate from the optimal design, thus affecting the operation and energyefficiency of the overall system. Therefore, it’s of high significance to constructsteady-state model, search global optimal set point according to the actual situation, andminimize the energy consumption on the basis of indoor air quality and human comfort.
For central air conditioning system composed of multiple subsystems, it is aneffective means to make integrated decision from the large-scale systems based on localcontrol optimization. The thesis focuses on the comprehensive needs of energy saving,indoor air quality and human comfort, to explore the overall solution to the system.
In the research, the VAV air conditioning system is decomposed in hierarchicalstructure on the basis of large-scale system decomposition-coordination theory andoptimal control strategy, and the overall hardware and software platform of theexperimental system and the design of its subsystem function are introduced.
The research was conducted from the perspectives of energy-saving strategies andcontrol algorithms. The subsystem dynamic model was constructed and the GPCalgorithm and Neural Network-PID control algorithm for local control unit wasdesigned. Herein, variable static pressure control strategy and demand controlventilation strategy were implemented based on the generalized predictive controlalgorithm and NN-PID control algorithm respectively. The experiments show that the algorithm with strong tracking and anti-jamming capability exhibits the considerablepotential for energy saving.
With the outdoor meteorological parameters adopted to predict the air-conditioningload, ASHRAE correction coefficient method was proposed to predict the outdoorhourly temperature. Thereby the training data set was constructed. The Elman neuralnetwork and Grey-NN neural network prediction algorithm were designed, and theforecast results of air conditioning system dynamic load could be the basis for theobjective function and constraints of the global optimization.
Steady-state optimization problem of VAV air conditioning system was studied,and the steady state model was established in the research. The global systemoptimization operating conditions model was constructed. An improved interbalancemethod (IBM) was designed to coordinate the global system, and the association couldbe equilibrium in the optimal point to ensure the convergence of coordination.Energy-saving optimization of the system control was implemented according to thesearched optimized value in each optimization cycle through the interaction between thetwo levels.
The experimental results show that, using global optimization strategy can be agood solution to the central air-conditioning control and optimization of the globalsystem with large energy saving potential in winter conditions. And the strategy can beextended to a class of large-scale conditions process systems.
引文
[1]任庆昌.楼宇自控系统的先进控制策略[J].智能建筑,2005,12:25-30.
[2] C. C. Chang, J. Zhao, N. Zhu. Energy saving effect prediction and post evaluation ofair-conditioning system in public buildings [J]. Energy and buildings,2011,43:3243-3249.
[3] Y.P. Zhou, J.Y. Wu, R.Z. Wang, S. Shiochi. Energy simulation in the variable refrigerant flowair-conditioning system under cooling conditions [J]. Energy and buildings,2007,39(2):212-220.
[4] S. Ginestet, D. Marchio. Control tuning of a simplified VAV system: Methodology and impacton energy consumption and IAQ [J]. Energy and buildings,2010,42(8):1205-1214.
[5] X.Y. Yan, Q.C Ren, Q.L. Meng. Global optimization of VAV air conditioning system [J].Proceedings of the world congress on intelligent control and automation (WCICA),2010:5077-5081.
[6] X.Y. Yan, Q.C Ren, Q.L. Meng. Iterative learning control in large scale HVAC system [J].Proceedings of the world congress on intelligent control and automation (WCICA),2010:5063-5066.
[7]黄敏珏.空调水系统VWV节能控制[J].制冷技术,2009,(4):11-16.
[8]王军,王雁,王瑞祥,邵惠鹤.一种优化控制变风量空调系统的新方法[J].上海交通大学学报,2006,40(2):248-252.
[9]符学伍,徐新华.变风量空调系统优化控制[J].山西建筑,2007,33(16):194-195.
[10]徐新华,王盛卫.多区域变风量空调系统优化控制研究[J].建筑热能通风空调,2006,25(6):1-4.
[11]魏燕红,晋欣桥,杜志敏,孙金龙.变风量空调末端再热器特性及优化控制研究[J].建筑热能通风空调,2007,26(6):1-5.
[12]赵廷法,王瑞华,王普.遗传算法在VAV中央空调优化控制中的应用[J].控制工程,2009,16:110-113.
[13]J. Liang, R.X. Du. Thermal comfort control based on neural network for HVAC application [J].Proceedings of the IEEE international conference on control applications,2005:819-824.
[14]曾庆雄,蔡龙俊.基于全局能耗的空调水系统运行策略的优化分析[J].建筑节能,2010,(3):27-29.
[15]王治学.地铁通风空调大系统的节能控制[J].智能建筑与城市信息,2008,(10):19-21.
[16]刘金平,麦粤帮,刘雪峰.中央空调系统变水温和变水量协调优化控制研究[J].建筑科学,2007,23(6):12-15.
[17]王静伟,贺利工,涂旭炜.地铁车站通风空调大系统的节能设计[J].城市轨道交通研究,2009,(5):38-40.
[18]石磊,王智伟,南小红.制冷空调DCS中的大系统观点[J].工程设计CAD与智能建筑,2002,(8):17-19.
[19]陈庆伟,姜建芳,胡维礼.分层递阶模糊控制在空调控制系统中的应用[J].南京理工大学学报,1994,(4):14-18.
[20]安婷,董增川.基于等流时带的水资源大系统优化分配模型[J].天津大学学报,2008,41(9):1073-1077.
[21]李新春,孙艳.综合人工智能优化矿业工程复杂大系统[J].系统工程学报,2002,17(4):379-384.
[22]上官甦.公路建设工程大系统土石方调配优化[J].公路交通科技,2006,23(4):37-40.
[23]万百五.工业大系统优化与产品质量控制[M].北京:科学出版社,2003年.
[24]K.M. Najla; R. Chokri; D. Mohamed; D. Nabil. Optimal control for discrete large scalenonlinear systems using hierarchical fuzzy systems [J]. ICMLC2010-The2nd internationalconference on machine learning and computing,2010:91-95.
[25]胡俊,李宏光.工业分层递阶控制中Petri网协调器的设计与实现[J].计算机集成制造系统,2007,13(12):2316-2321.
[26]M. Mossolly, K. Ghali, N. Ghaddar. Optimal control strategy for a multi-zone air conditioningsystem using a genetic algorithm [J]. Energy,2009,34(1):58-66.
[27]钱富才,刘林运,万百五.一类大系统的稳态递阶优化控制算法[J].西安石油学院学报,1998,13(1):59-61.
[28]焦宝聪,万百五.可分非凸稳态大系统凸化方法研究[J].西安交通大学学报,1993,27(3):81-89.
[29]万百五,黄正良.大工业过程计算机在线稳态优化控制[M].北京:科学出版社,1998.
[30]李少远.全局工况系统预测控制及其应用[M].北京:科学出版社,2008.
[31]M.R. Davoodi, H.R. Momeni, A.Daryabor. Conservation reduction in two-level control forstabilization of discrete large scale systems [J]. Acta automatica sinica,2010,36(2):322-327.
[32]J.S.H Tsai, Y.Y. Du, P.H. Huang, S.M. Guo, L.S. Shieh, Y.H. Chen. Interative learning-baseddecentralized adaptive tracker for large-scale systems: A digital redesign approach [J]. ISATransactions. In press.
[33]朱靖华,王光远.多级递阶工程系统全局优化抗震设计的混合法[J].土木工程学报,2005,38(4):18-24.
[34]崔云飞,王帅,李艺,董可为.基于多级递阶控制结构的云计算资源管理研究[J].装备指挥技术学院学报,2010,21(2):117-122.
[35]曹卫华,段平,吴敏,向婕.基于分级递阶结构的铁矿石烧结过程智能控制[J].仪器仪表学报,2010,31(3):553-557.
[36]周盛强.一类基于分解协调机制的多学科优化算法[J].航空计算技术,2006,36(1):117-121.
[37]I. Zamania, N. Sadatib, M.H. Zarifd. On the stability issues for fuzzy large-scale systems [J].Fuzzy sets and systems,2011,174(1):31-49.
[38]W. Xie, I. Bonis, C.Theodoropoulos. Off-line model reduction for on-line linear MPC ofnonlinear large-scale distributed systems [J]. Computers and chemical engineering,2011,35:750-757.
[39]B. Karimi, M.B. Menhaj. Non-affine nonlinear adaptive control of decentralized large-scalesystems using neural networks [J]. Information sciences,2010,180:3335-3347.
[40]王建玉,任庆昌.基于协调的变风量空调系统分布式预测控制[J].信息与控制,2010,39(5):651-656.
[41]王建玉,任庆昌.变风量空调系统的分布式预测控制[J].计算机工程与应用,2008,44(5):225-228.
[42]Z. Hou, Z. Lian, Y. Yao, X. Yuan. Cooling-load prediction by the combination of rough settheory and an artificial neural-network based on data-fusion technique [J]. Applied energy,2006,83:1033-1046.
[43]石磊.基于负荷预测在线修正的冰蓄冷空调系统优化运行研究[D].西安:西安建筑科技大学,2002.
[44]Q. Zhou, S.W. Wang, X.H. Xu, F. Xiao. A Grey-Box model of next-day building thermal loadprediction for energy-efficient control [J]. International journal of energy research,2008,32(15):1418-1431.
[45]叶大法,杨国荣.变风量空调系统设计[M].北京:中国建筑工业出版社,2007.
[46]马素贞,刘传聚.变风量空调系统发展状况[J].暖通空调,2007,37(1):33-36.
[47]晋欣桥,夏凊,周兴禧,王盛卫.多区域变风量空调系统送风温度的优化节能控制[J].上海交通大学学报,2000,34(4):507-512.
[48]孟华,王盛卫,龙惟定.空调水系统实时在线优化控制预测模型的研究[J].同济大学学报(自然科学版),2006,34(5):670-674.
[49]X.H. Xu, S.W. Wang, Z.W. Sun, F. Xiao. A model-based optimal ventilation control strategy ofmulti-zone VAV air-conditioning systems using genetic algorithm [J]. Applied thermalengineering,2009,29(1):91-104.
[50]S. Atthajariyakul, T. Leephakpreeda. Real-time determination of optimal indoor-air conditioinfor thermal comfort, air quality and efficient energy usage [J]. Energy and buildings,2004,36:720-733.
[51]林杰,万百五.序列凸化技术(SCM)及其在大系统优化技术中的应用[J].系统工程学报,1990,5(1):50-63.
[52]钱富才,李琦,万百五.可分非凸稳态大系统的全局递阶优化控制算法[J].西安交通大学学报,2000,34(12):44-46.
[53]陈庆,李少远,席裕庚.基于全局最优的生成全过程分布式预测控制[J].上海交通大学学报,2005,39(3):349-352.
[54]陈庆,李少远,席裕庚.一类串联生产过程的分布式解耦预测控制[J].控制与决策,2004,19(6):647-650.
[55]丁宝苍,席裕庚.基于Kleinman控制器的广义预测控制系统的稳定性分析[J].上海交通大学学报,2005,34(2):176-189.
[56]X.M. Hua, S. Rohani, A. Jutan. Cascade colosed-loop optimization and control of batch reactors[J]. Chemical engineering science,2004,59(24):5695-5708.
[57]Y. Yao, J. Chen. Global optimization of a central air-conditioning system using decomposition-coordination method [J]. Energy and buildings,2010,42,570-583.
[58]T. Perumal, A.R. Ramli, C.Y. Leong, K. Samsudin, S. Mansor. Middleware for heterogeneoussubsystems interoperability in intelligent buildings [J]. Automation in construction,2010,19:160-168.
[59]H. Doukas, K.D. Patlitzianas, K. Iatropoulos, J. Psarras. Intelligent building energy managementsystem using rule sets [J]. Building and environment2007,42:3562–3569
[60]于仲安,李慧斌.工业控制网络和信息网络的集成技术[J].工矿自动化,2008,1:76-78.
[61]雍静,刘利萍,周健.基于Web数据库技术的智能建筑中央空调系统网络集成[J].低压电器,2007,6:53-56.
[62]周路明,欧阳松.智能建筑管理系统数据库模块的研究和设计[J].计算技术与自动化,2007,26(4):95-98.
[63]C.H. Su, S.L. Chien. Data storage practices and query processing in XML databases: A survey[J]. Knowledge-based systems,2011,24(8):1317-1340.
[64]K.M. Jun, H.L. Chun, W.C. Chin. XTRON: An XML data management system using relationaldatabases [J]. Information and software technology,2008,50:462–479.
[65]廖国富,杨戈方,杨柳.基于OPC XML的建筑智能化系统集成模型研究[J].黄冈师范学院学报,2007,6:103-104.
[66]郑珂,徐艳群,张斌.基于FDT与OPC XML的工业控制系统集成研究[J].计算机测量与控制,2010,18(8):1805-1807.
[67]S. Subramaniam, C.H. Su, P.K. Hoong. s-XML: An efficient mapping scheme to bridge XMLand relational database [J]. Knowledge-based systems,2012,27:369-380.
[68]雷晓凤.变风量空调系统能耗分析与空气品质控制研究[D].西安:西安建筑科技大学,2011.
[69]L.A. Zadeh. From circuit theory to system theory [J]. Proceedings of the IRE,1962:856-865.
[70]侯媛彬,汪梅,王立琦.系统辨识及其Matlab仿真[M].北京:科学出版社,2004.
[71]倪博溢,萧德云. Matlab环境下的系统辨识仿真工具箱[J].系统仿真学报,2006,18(6):1493-1496.
[72]任庆昌,于军琪.建模与辨识[M].西安:西安建筑科技大学,2009.
[73]L. Ljung, T. Soderstrom. Theory and practice of recursive identification [M]. London: The MITpress,1983.
[74]方崇智,萧德云.过程辨识[M].北京:清华大学出版社,1988,8.
[75]闫秀英.变风量（VAV）中央空调系统递阶优化控制与节能技术研究[D].西安:西安建筑科技大学,2010.
[76]D.W. Clarke, C. Mohtadi, P.S. Tuffs. Generalized predictive control-Part I [J]. The basicalgorithm. Automatica,1987,23(2):137-148.
[77]席裕庚.预测控制[M].北京:国防工业出版社,1993.
[78]陈志旺.直接广义预测控制算法研究[D].秦皇岛市:燕山大学,2007.
[79]胡耀华贾欣乐.广义预测控制综述[J].信息与控制,2000,29(3):248-256.
[80]段飞跃,任庆昌.广义预测控制在蓄冰空调融冰环节中的应用[J].装备制造技术,2009,(6):79-80.
[81]杨洪祥.基于广义预测控制的变风量空调末端仿真与控制研究[D].北京:北京工业大学,2009.
[82]李绍勇,崔旭春,王刚,王瑛,闰树龙,杨蓉霞,韩喜莲.空调房间室温广义预测控制的仿真研究[J].建筑热能通风空调,2003,22(6):23-26.
[83]杨洪祥.变风量空调末端广义预测自校正控制[J].控制工程,2009,16:57-60.
[84]Xu M, Li S Y. Practical generalized predictive control with decentralized identification approachto HVAC systems [J]. Energy conversion and management,2007,48:292–299
[85]叶大法,杨国荣,董涛.国内外变风量空调系统设计理念探讨与借鉴[J].暖通空调,2008,38(3):62-67.
[86]N. Nassif. A robust CO2-based demand-controlled ventilation control strategy for multi-zoneHVAC systems [J]. Energy and Buildings,2012,45:72–81.
[87]T. Lu, X.S. Lü, M. Viljanen. A novel and dynamic demand-controlled ventilation strategy forCO2control and energy saving in buildings [J]. Energy and Buildings,2011,43(9):2499-2508.
[88]B.R. Srensen. An energy efficient control strategy for fan static pressure difference [J].Proceedings of2011International conference on consumer electronics, communications andnetworks,2011:4736-4739.
[89]戴斌文,狄洪发,江亿.变风量空调系统风机总风量控制方法[J].暖通空调,1999,29(3):1-6.
[90]赵相彬,赵哲身,孙怡佳. VAV空调系统的变静压控制方法研究和展望[J].智能建筑,2005(2):12-16.
[91]C. Wu, Y.J. Lei. Study on fuzzy control of supply air static pressure for a marine variable-air-volume air-conditioning system [J]. Proceedings-2010international conference onintelligent system design and engineering application,2010,(2):445-448.
[92]孟华,龙惟定,王盛卫.基于遗传算法的空调水系统优化控制研究[J].建筑节能,2007,35(191):39-42.
[93]闫秀英,任庆昌,孟庆龙.变风量空调系统的迭代学习控制研究[J].计算机工程与应用,2011,47(16):211-213.
[94]S.H. Zou, Q. Li, M.C. Yu. The research of air quality and its control in air conditioning room [J].Engineering science,2007,9(3):82-86.
[95]T. Lu, X.S. Lü, M. Viljanen. A novel and dynamic demand-controlled ventilation strategy forCO2control and energy saving in buildings [J]. Energy and buildings,2011,43(9):2499-2508.
[96]C.Y.H. Chao, J.S. Hu. Development of a dual-mode demand control ventilation strategy forindoor air quality control and energy saving [J]. Building and environment,2004,39:385–397.
[97]邹声华,李强,于梅春.空调房间的空气品质及其控制研究[J].中国工程科学,2007,9(3):82-86.
[98]Persily, A K, W S Dols. The relation of CO2concentration to office building ventilation-airchange rate and airtightness in buildings [J]. ASTMSTP,1990,1067:77-92.
[99]何大四,童山中,范晓伟.会议室内基于CO2浓度的新风节能控制仿真研究[J].建筑热能通风空调,2009,28(2):80-82.
[100]刘金琨.先进PID控制MATLAB仿真[M].北京:电子工业出版社,2007.
[101]陆亚俊,马最良,邹平华.暖通空调[M].北京:中国建筑工业出版社,2007.
[102]S. Boonyatikam, J.R. Jones. Smart building control strategies research and application [J].ASHRAE Transactions,1989,95(1):557-562.
[103]M. Kawashima, C.E. Dorgan, J.W. Mitchell. Hourly thermal load prediction for the next24hours by ARIMA, EWMA, LR and an artificial neural network [J]. ASHRAE Transactions,1995,101(1):186-200.
[104]何大四.暖通空调中气象数据相关的几个问题探讨[D].上海:同济大学,2006.
[105]R.A. Hooshmand, H. Amooshahi, M. Parastegari. A hybrid intelligent algorithm basedshort-term load forecasting approach [J]. Electrical power and energy systems,2013,45:313-324.
[106]李玉云,王永骥.人工神经网络在暖通空调领域的应用研究发展[J].暖通空调,2001,31(1):38-41.
[107]M. Mohanraja, S. Jayaraj, C. Muraleedharan. Applications of artificial neural networks forrefrigeration, air-conditioning and heat pump systems-A review [J]. Renewable and sustainableenergy reviews,2012,16:1340-1358.
[108]M. Anstett, J.F. Kreider. Application of neural networking models to predict energy use [J].ASHRAE Transactions,1993,99(1):505-517.
[109]J.F. Kreider, S.L. Blanc, R.C. Kammerud, P.S. Curtiss. Operational data as the basis for neuralnetwork prediction of hourly elctrical demand [J]. ASHRAE transactions,1997,103:926-934.
[110]G.L. Gibson, T.T. Kraft. Electric demand predictioin using artificial neural network technology[J]. ASHRAE Journal,1993(3):60-68.
[111]K. Sanjay, D. Sanjay. A wavelet Elman neural network for short-term electrical load predictionunder the influence of temperature [J]. Electrical power and energy systems,2012,43:1063-1071.
[112]崔高健,凡东生,曲永利.基于Elman型神经网络集中供热负荷预测模型的研究[J].建筑节能,2011,39(241):9-11.
[113]史峰,王小川,郁磊,李洋. MATLAB神经网络30个案例分析[M].北京:北京航空航天大学出版社,2010.
[114]Y.W. Wang, W.J. Cai, Y.C. Soh, S.J. Li, L. Lu, L.H. Xie. A simplified modeling of coolingcoils for control and optimization of HVAC systems [J]. Energy conversion and management,2004(45):2915-2930.
[115]D. Li, X.L. Sun, M.P. Biswal, F. Gao. Convexification and monotonization in globaloptimization [J], Annals of operation research,2001(105):213-226.
[116]L.S. Zhang, C.K. NG, D. Li, W.W. Tian. A new filled function method for global optimization[J], Journal of global optimization,2004(28):17-43.
[117]W. Findeisen,F. Bailey, M. Brdys, K. Malinovski, P. Tatjewski, A. Wozniak. Control andcoordination in hierarchial systems [M]. John Wiley and Sons,1980.
[118]钱富才,万百五.稳态大系统中关联平衡法的改进[J].系统工程理论与实践,1998,(8):87-90.
[119]L. Lu, W.J. Cai, Y.S. Chai, L.H. Xie. Global optimization for overall HVAC systems–Part Iproblem formulation and analysis [J]. Energy Conversion and Management,2005,46:999-1014.
[120]何厚键.中央空调水系统的建模与优化研究[D].沈阳:沈阳工业大学,2005.
[121]孙学德.建筑环境设备能耗优化[D].南京:南京航空航天大学,2009.
[122]靳路明.水泵效率曲线的拟合[J].河北农业大学学报,1992,15(3):87-90.
[123]陈文凭,杨昌智,余院生.基于冷水机组性能曲线的中央空调水系统优化控制[J].流体机械,2008,36(8):73-78.
[124]American Society of Heating, Refrigerating and Air-Conditioning Engineers, ASHRAEhandbook-system and equipment[M], USA: ASHRAE Inc.,2000.
[125]X.B. Yang, X.Q. Jin, Z.M. Du, B. Fan, X.F. Chai. Evaluation of four control strategies forbuilding VAV air-conditioning systems [J]. Energy and Buildings,2011,43:414-422.
[126]姚晔.集中空调系统状态空间建模及稳态优化节能控制研究[D].上海:上海交通大学,2005.
[127]冯艳青.多元函数凸性的判断及应用,西南民族学院学报·自然科学版[J],2001,27(4):473-474.
[2] C. C. Chang, J. Zhao, N. Zhu. Energy saving effect prediction and post evaluation ofair-conditioning system in public buildings [J]. Energy and buildings,2011,43:3243-3249.
[3] Y.P. Zhou, J.Y. Wu, R.Z. Wang, S. Shiochi. Energy simulation in the variable refrigerant flowair-conditioning system under cooling conditions [J]. Energy and buildings,2007,39(2):212-220.
[4] S. Ginestet, D. Marchio. Control tuning of a simplified VAV system: Methodology and impacton energy consumption and IAQ [J]. Energy and buildings,2010,42(8):1205-1214.
[5] X.Y. Yan, Q.C Ren, Q.L. Meng. Global optimization of VAV air conditioning system [J].Proceedings of the world congress on intelligent control and automation (WCICA),2010:5077-5081.
[6] X.Y. Yan, Q.C Ren, Q.L. Meng. Iterative learning control in large scale HVAC system [J].Proceedings of the world congress on intelligent control and automation (WCICA),2010:5063-5066.
[7]黄敏珏.空调水系统VWV节能控制[J].制冷技术,2009,(4):11-16.
[8]王军,王雁,王瑞祥,邵惠鹤.一种优化控制变风量空调系统的新方法[J].上海交通大学学报,2006,40(2):248-252.
[9]符学伍,徐新华.变风量空调系统优化控制[J].山西建筑,2007,33(16):194-195.
[10]徐新华,王盛卫.多区域变风量空调系统优化控制研究[J].建筑热能通风空调,2006,25(6):1-4.
[11]魏燕红,晋欣桥,杜志敏,孙金龙.变风量空调末端再热器特性及优化控制研究[J].建筑热能通风空调,2007,26(6):1-5.
[12]赵廷法,王瑞华,王普.遗传算法在VAV中央空调优化控制中的应用[J].控制工程,2009,16:110-113.
[13]J. Liang, R.X. Du. Thermal comfort control based on neural network for HVAC application [J].Proceedings of the IEEE international conference on control applications,2005:819-824.
[14]曾庆雄,蔡龙俊.基于全局能耗的空调水系统运行策略的优化分析[J].建筑节能,2010,(3):27-29.
[15]王治学.地铁通风空调大系统的节能控制[J].智能建筑与城市信息,2008,(10):19-21.
[16]刘金平,麦粤帮,刘雪峰.中央空调系统变水温和变水量协调优化控制研究[J].建筑科学,2007,23(6):12-15.
[17]王静伟,贺利工,涂旭炜.地铁车站通风空调大系统的节能设计[J].城市轨道交通研究,2009,(5):38-40.
[18]石磊,王智伟,南小红.制冷空调DCS中的大系统观点[J].工程设计CAD与智能建筑,2002,(8):17-19.
[19]陈庆伟,姜建芳,胡维礼.分层递阶模糊控制在空调控制系统中的应用[J].南京理工大学学报,1994,(4):14-18.
[20]安婷,董增川.基于等流时带的水资源大系统优化分配模型[J].天津大学学报,2008,41(9):1073-1077.
[21]李新春,孙艳.综合人工智能优化矿业工程复杂大系统[J].系统工程学报,2002,17(4):379-384.
[22]上官甦.公路建设工程大系统土石方调配优化[J].公路交通科技,2006,23(4):37-40.
[23]万百五.工业大系统优化与产品质量控制[M].北京:科学出版社,2003年.
[24]K.M. Najla; R. Chokri; D. Mohamed; D. Nabil. Optimal control for discrete large scalenonlinear systems using hierarchical fuzzy systems [J]. ICMLC2010-The2nd internationalconference on machine learning and computing,2010:91-95.
[25]胡俊,李宏光.工业分层递阶控制中Petri网协调器的设计与实现[J].计算机集成制造系统,2007,13(12):2316-2321.
[26]M. Mossolly, K. Ghali, N. Ghaddar. Optimal control strategy for a multi-zone air conditioningsystem using a genetic algorithm [J]. Energy,2009,34(1):58-66.
[27]钱富才,刘林运,万百五.一类大系统的稳态递阶优化控制算法[J].西安石油学院学报,1998,13(1):59-61.
[28]焦宝聪,万百五.可分非凸稳态大系统凸化方法研究[J].西安交通大学学报,1993,27(3):81-89.
[29]万百五,黄正良.大工业过程计算机在线稳态优化控制[M].北京:科学出版社,1998.
[30]李少远.全局工况系统预测控制及其应用[M].北京:科学出版社,2008.
[31]M.R. Davoodi, H.R. Momeni, A.Daryabor. Conservation reduction in two-level control forstabilization of discrete large scale systems [J]. Acta automatica sinica,2010,36(2):322-327.
[32]J.S.H Tsai, Y.Y. Du, P.H. Huang, S.M. Guo, L.S. Shieh, Y.H. Chen. Interative learning-baseddecentralized adaptive tracker for large-scale systems: A digital redesign approach [J]. ISATransactions. In press.
[33]朱靖华,王光远.多级递阶工程系统全局优化抗震设计的混合法[J].土木工程学报,2005,38(4):18-24.
[34]崔云飞,王帅,李艺,董可为.基于多级递阶控制结构的云计算资源管理研究[J].装备指挥技术学院学报,2010,21(2):117-122.
[35]曹卫华,段平,吴敏,向婕.基于分级递阶结构的铁矿石烧结过程智能控制[J].仪器仪表学报,2010,31(3):553-557.
[36]周盛强.一类基于分解协调机制的多学科优化算法[J].航空计算技术,2006,36(1):117-121.
[37]I. Zamania, N. Sadatib, M.H. Zarifd. On the stability issues for fuzzy large-scale systems [J].Fuzzy sets and systems,2011,174(1):31-49.
[38]W. Xie, I. Bonis, C.Theodoropoulos. Off-line model reduction for on-line linear MPC ofnonlinear large-scale distributed systems [J]. Computers and chemical engineering,2011,35:750-757.
[39]B. Karimi, M.B. Menhaj. Non-affine nonlinear adaptive control of decentralized large-scalesystems using neural networks [J]. Information sciences,2010,180:3335-3347.
[40]王建玉,任庆昌.基于协调的变风量空调系统分布式预测控制[J].信息与控制,2010,39(5):651-656.
[41]王建玉,任庆昌.变风量空调系统的分布式预测控制[J].计算机工程与应用,2008,44(5):225-228.
[42]Z. Hou, Z. Lian, Y. Yao, X. Yuan. Cooling-load prediction by the combination of rough settheory and an artificial neural-network based on data-fusion technique [J]. Applied energy,2006,83:1033-1046.
[43]石磊.基于负荷预测在线修正的冰蓄冷空调系统优化运行研究[D].西安:西安建筑科技大学,2002.
[44]Q. Zhou, S.W. Wang, X.H. Xu, F. Xiao. A Grey-Box model of next-day building thermal loadprediction for energy-efficient control [J]. International journal of energy research,2008,32(15):1418-1431.
[45]叶大法,杨国荣.变风量空调系统设计[M].北京:中国建筑工业出版社,2007.
[46]马素贞,刘传聚.变风量空调系统发展状况[J].暖通空调,2007,37(1):33-36.
[47]晋欣桥,夏凊,周兴禧,王盛卫.多区域变风量空调系统送风温度的优化节能控制[J].上海交通大学学报,2000,34(4):507-512.
[48]孟华,王盛卫,龙惟定.空调水系统实时在线优化控制预测模型的研究[J].同济大学学报(自然科学版),2006,34(5):670-674.
[49]X.H. Xu, S.W. Wang, Z.W. Sun, F. Xiao. A model-based optimal ventilation control strategy ofmulti-zone VAV air-conditioning systems using genetic algorithm [J]. Applied thermalengineering,2009,29(1):91-104.
[50]S. Atthajariyakul, T. Leephakpreeda. Real-time determination of optimal indoor-air conditioinfor thermal comfort, air quality and efficient energy usage [J]. Energy and buildings,2004,36:720-733.
[51]林杰,万百五.序列凸化技术(SCM)及其在大系统优化技术中的应用[J].系统工程学报,1990,5(1):50-63.
[52]钱富才,李琦,万百五.可分非凸稳态大系统的全局递阶优化控制算法[J].西安交通大学学报,2000,34(12):44-46.
[53]陈庆,李少远,席裕庚.基于全局最优的生成全过程分布式预测控制[J].上海交通大学学报,2005,39(3):349-352.
[54]陈庆,李少远,席裕庚.一类串联生产过程的分布式解耦预测控制[J].控制与决策,2004,19(6):647-650.
[55]丁宝苍,席裕庚.基于Kleinman控制器的广义预测控制系统的稳定性分析[J].上海交通大学学报,2005,34(2):176-189.
[56]X.M. Hua, S. Rohani, A. Jutan. Cascade colosed-loop optimization and control of batch reactors[J]. Chemical engineering science,2004,59(24):5695-5708.
[57]Y. Yao, J. Chen. Global optimization of a central air-conditioning system using decomposition-coordination method [J]. Energy and buildings,2010,42,570-583.
[58]T. Perumal, A.R. Ramli, C.Y. Leong, K. Samsudin, S. Mansor. Middleware for heterogeneoussubsystems interoperability in intelligent buildings [J]. Automation in construction,2010,19:160-168.
[59]H. Doukas, K.D. Patlitzianas, K. Iatropoulos, J. Psarras. Intelligent building energy managementsystem using rule sets [J]. Building and environment2007,42:3562–3569
[60]于仲安,李慧斌.工业控制网络和信息网络的集成技术[J].工矿自动化,2008,1:76-78.
[61]雍静,刘利萍,周健.基于Web数据库技术的智能建筑中央空调系统网络集成[J].低压电器,2007,6:53-56.
[62]周路明,欧阳松.智能建筑管理系统数据库模块的研究和设计[J].计算技术与自动化,2007,26(4):95-98.
[63]C.H. Su, S.L. Chien. Data storage practices and query processing in XML databases: A survey[J]. Knowledge-based systems,2011,24(8):1317-1340.
[64]K.M. Jun, H.L. Chun, W.C. Chin. XTRON: An XML data management system using relationaldatabases [J]. Information and software technology,2008,50:462–479.
[65]廖国富,杨戈方,杨柳.基于OPC XML的建筑智能化系统集成模型研究[J].黄冈师范学院学报,2007,6:103-104.
[66]郑珂,徐艳群,张斌.基于FDT与OPC XML的工业控制系统集成研究[J].计算机测量与控制,2010,18(8):1805-1807.
[67]S. Subramaniam, C.H. Su, P.K. Hoong. s-XML: An efficient mapping scheme to bridge XMLand relational database [J]. Knowledge-based systems,2012,27:369-380.
[68]雷晓凤.变风量空调系统能耗分析与空气品质控制研究[D].西安:西安建筑科技大学,2011.
[69]L.A. Zadeh. From circuit theory to system theory [J]. Proceedings of the IRE,1962:856-865.
[70]侯媛彬,汪梅,王立琦.系统辨识及其Matlab仿真[M].北京:科学出版社,2004.
[71]倪博溢,萧德云. Matlab环境下的系统辨识仿真工具箱[J].系统仿真学报,2006,18(6):1493-1496.
[72]任庆昌,于军琪.建模与辨识[M].西安:西安建筑科技大学,2009.
[73]L. Ljung, T. Soderstrom. Theory and practice of recursive identification [M]. London: The MITpress,1983.
[74]方崇智,萧德云.过程辨识[M].北京:清华大学出版社,1988,8.
[75]闫秀英.变风量（VAV）中央空调系统递阶优化控制与节能技术研究[D].西安:西安建筑科技大学,2010.
[76]D.W. Clarke, C. Mohtadi, P.S. Tuffs. Generalized predictive control-Part I [J]. The basicalgorithm. Automatica,1987,23(2):137-148.
[77]席裕庚.预测控制[M].北京:国防工业出版社,1993.
[78]陈志旺.直接广义预测控制算法研究[D].秦皇岛市:燕山大学,2007.
[79]胡耀华贾欣乐.广义预测控制综述[J].信息与控制,2000,29(3):248-256.
[80]段飞跃,任庆昌.广义预测控制在蓄冰空调融冰环节中的应用[J].装备制造技术,2009,(6):79-80.
[81]杨洪祥.基于广义预测控制的变风量空调末端仿真与控制研究[D].北京:北京工业大学,2009.
[82]李绍勇,崔旭春,王刚,王瑛,闰树龙,杨蓉霞,韩喜莲.空调房间室温广义预测控制的仿真研究[J].建筑热能通风空调,2003,22(6):23-26.
[83]杨洪祥.变风量空调末端广义预测自校正控制[J].控制工程,2009,16:57-60.
[84]Xu M, Li S Y. Practical generalized predictive control with decentralized identification approachto HVAC systems [J]. Energy conversion and management,2007,48:292–299
[85]叶大法,杨国荣,董涛.国内外变风量空调系统设计理念探讨与借鉴[J].暖通空调,2008,38(3):62-67.
[86]N. Nassif. A robust CO2-based demand-controlled ventilation control strategy for multi-zoneHVAC systems [J]. Energy and Buildings,2012,45:72–81.
[87]T. Lu, X.S. Lü, M. Viljanen. A novel and dynamic demand-controlled ventilation strategy forCO2control and energy saving in buildings [J]. Energy and Buildings,2011,43(9):2499-2508.
[88]B.R. Srensen. An energy efficient control strategy for fan static pressure difference [J].Proceedings of2011International conference on consumer electronics, communications andnetworks,2011:4736-4739.
[89]戴斌文,狄洪发,江亿.变风量空调系统风机总风量控制方法[J].暖通空调,1999,29(3):1-6.
[90]赵相彬,赵哲身,孙怡佳. VAV空调系统的变静压控制方法研究和展望[J].智能建筑,2005(2):12-16.
[91]C. Wu, Y.J. Lei. Study on fuzzy control of supply air static pressure for a marine variable-air-volume air-conditioning system [J]. Proceedings-2010international conference onintelligent system design and engineering application,2010,(2):445-448.
[92]孟华,龙惟定,王盛卫.基于遗传算法的空调水系统优化控制研究[J].建筑节能,2007,35(191):39-42.
[93]闫秀英,任庆昌,孟庆龙.变风量空调系统的迭代学习控制研究[J].计算机工程与应用,2011,47(16):211-213.
[94]S.H. Zou, Q. Li, M.C. Yu. The research of air quality and its control in air conditioning room [J].Engineering science,2007,9(3):82-86.
[95]T. Lu, X.S. Lü, M. Viljanen. A novel and dynamic demand-controlled ventilation strategy forCO2control and energy saving in buildings [J]. Energy and buildings,2011,43(9):2499-2508.
[96]C.Y.H. Chao, J.S. Hu. Development of a dual-mode demand control ventilation strategy forindoor air quality control and energy saving [J]. Building and environment,2004,39:385–397.
[97]邹声华,李强,于梅春.空调房间的空气品质及其控制研究[J].中国工程科学,2007,9(3):82-86.
[98]Persily, A K, W S Dols. The relation of CO2concentration to office building ventilation-airchange rate and airtightness in buildings [J]. ASTMSTP,1990,1067:77-92.
[99]何大四,童山中,范晓伟.会议室内基于CO2浓度的新风节能控制仿真研究[J].建筑热能通风空调,2009,28(2):80-82.
[100]刘金琨.先进PID控制MATLAB仿真[M].北京:电子工业出版社,2007.
[101]陆亚俊,马最良,邹平华.暖通空调[M].北京:中国建筑工业出版社,2007.
[102]S. Boonyatikam, J.R. Jones. Smart building control strategies research and application [J].ASHRAE Transactions,1989,95(1):557-562.
[103]M. Kawashima, C.E. Dorgan, J.W. Mitchell. Hourly thermal load prediction for the next24hours by ARIMA, EWMA, LR and an artificial neural network [J]. ASHRAE Transactions,1995,101(1):186-200.
[104]何大四.暖通空调中气象数据相关的几个问题探讨[D].上海:同济大学,2006.
[105]R.A. Hooshmand, H. Amooshahi, M. Parastegari. A hybrid intelligent algorithm basedshort-term load forecasting approach [J]. Electrical power and energy systems,2013,45:313-324.
[106]李玉云,王永骥.人工神经网络在暖通空调领域的应用研究发展[J].暖通空调,2001,31(1):38-41.
[107]M. Mohanraja, S. Jayaraj, C. Muraleedharan. Applications of artificial neural networks forrefrigeration, air-conditioning and heat pump systems-A review [J]. Renewable and sustainableenergy reviews,2012,16:1340-1358.
[108]M. Anstett, J.F. Kreider. Application of neural networking models to predict energy use [J].ASHRAE Transactions,1993,99(1):505-517.
[109]J.F. Kreider, S.L. Blanc, R.C. Kammerud, P.S. Curtiss. Operational data as the basis for neuralnetwork prediction of hourly elctrical demand [J]. ASHRAE transactions,1997,103:926-934.
[110]G.L. Gibson, T.T. Kraft. Electric demand predictioin using artificial neural network technology[J]. ASHRAE Journal,1993(3):60-68.
[111]K. Sanjay, D. Sanjay. A wavelet Elman neural network for short-term electrical load predictionunder the influence of temperature [J]. Electrical power and energy systems,2012,43:1063-1071.
[112]崔高健,凡东生,曲永利.基于Elman型神经网络集中供热负荷预测模型的研究[J].建筑节能,2011,39(241):9-11.
[113]史峰,王小川,郁磊,李洋. MATLAB神经网络30个案例分析[M].北京:北京航空航天大学出版社,2010.
[114]Y.W. Wang, W.J. Cai, Y.C. Soh, S.J. Li, L. Lu, L.H. Xie. A simplified modeling of coolingcoils for control and optimization of HVAC systems [J]. Energy conversion and management,2004(45):2915-2930.
[115]D. Li, X.L. Sun, M.P. Biswal, F. Gao. Convexification and monotonization in globaloptimization [J], Annals of operation research,2001(105):213-226.
[116]L.S. Zhang, C.K. NG, D. Li, W.W. Tian. A new filled function method for global optimization[J], Journal of global optimization,2004(28):17-43.
[117]W. Findeisen,F. Bailey, M. Brdys, K. Malinovski, P. Tatjewski, A. Wozniak. Control andcoordination in hierarchial systems [M]. John Wiley and Sons,1980.
[118]钱富才,万百五.稳态大系统中关联平衡法的改进[J].系统工程理论与实践,1998,(8):87-90.
[119]L. Lu, W.J. Cai, Y.S. Chai, L.H. Xie. Global optimization for overall HVAC systems–Part Iproblem formulation and analysis [J]. Energy Conversion and Management,2005,46:999-1014.
[120]何厚键.中央空调水系统的建模与优化研究[D].沈阳:沈阳工业大学,2005.
[121]孙学德.建筑环境设备能耗优化[D].南京:南京航空航天大学,2009.
[122]靳路明.水泵效率曲线的拟合[J].河北农业大学学报,1992,15(3):87-90.
[123]陈文凭,杨昌智,余院生.基于冷水机组性能曲线的中央空调水系统优化控制[J].流体机械,2008,36(8):73-78.
[124]American Society of Heating, Refrigerating and Air-Conditioning Engineers, ASHRAEhandbook-system and equipment[M], USA: ASHRAE Inc.,2000.
[125]X.B. Yang, X.Q. Jin, Z.M. Du, B. Fan, X.F. Chai. Evaluation of four control strategies forbuilding VAV air-conditioning systems [J]. Energy and Buildings,2011,43:414-422.
[126]姚晔.集中空调系统状态空间建模及稳态优化节能控制研究[D].上海:上海交通大学,2005.
[127]冯艳青.多元函数凸性的判断及应用,西南民族学院学报·自然科学版[J],2001,27(4):473-474.