办公建筑空调系统用能优化研究
|
摘要
持续增长的空调能量消耗和电力需求不断挑战着空调用户和电力供应商。办公建筑空调系统存在能效低下及能量浪费严重的现象,因此依据不同的电价机制,对空调系统能量消耗进行优化,实现空调用户和电力供应商的双赢,是本文研究的出发点。能量利用效率是转换效率、传输效率和终端使用效率相乘得到的,终端使用效率的提高,可以减少整个能源转换和传输链的消耗。而目前常采用的空调系统能量分析方法,主要注重空调系统的传输转换链这一环节。因此,寻求一种从使用效率开始,接着逆向分析传输转换效率的空调系统能量使用分析方法,是目前亟待解决的问题。针对该问题,不同电价机制下的室内冷负荷的需求优化是提高空调系统使用效率的有效方法。而基于空调系统能量输入比的概念,对风机盘管空调和变风量空调的传输转换链进行了优化,是提高空调系统传输转换效率的主要手段。
本文对不同电价机制下的办公建筑空调系统的优化方法进行了深入的研究,主要的研究工作概括如下：
1、针对在不同电价机制下,空调系统基线冷负荷难以预测的问题,提出一种基于模糊C-均值聚类预处理的神经网络的空调系统冷负荷基线预测方法。采用模糊C-均值聚类的方法对预测日各时刻的室外气象数据进行了聚类；在此基础上,采用BP神经网络对大量的训练样本进行了网络训练,得到了各时刻的负荷预测模型；实验结果表明该预测模型整体预测精度都高于线性模型,对于小时负荷变化较大的时刻,更是显示了该模型的优越性。
2、针对在固定电价下,空调系统室内温度设定值常采用固定设定值的问题,提出一种根据室外逐时温度动态设定室内温度值的方法。通过获得的动态室内温度设定值,可以得到空调系统的最小冷负荷需求。首先,以一间典型的办公房间为例,通过构建一个带有热物质的简化模型,建立了空调冷负荷与室内温度设定值和室外逐时温度的热平衡方程；然后,对舒适度指标公式进行了简化,建立了舒适度指标与室内温度设定值和热物质内表面温度的关系模型；最后,基于上述模型,建立了以空调系统冷负荷需求最小为目标,舒适度指标为约束,室内温度设定值为决策变量的优化模型。实验结果表明建筑热物质能够延迟冷负荷峰值的产生时间,具有削峰填谷的作用。动态温度设定值相比于26℃固定温度设定值,不仅空调冷负荷需求减少了7%,而且还能改善PMV的指标。优化后的固定电价室内冷负荷与基线冷负荷相比,削减了11.2%。
3、针对在分时电价下,采用预冷控制策略时,室内冷负荷需求难以预测的问题,提出一种以空调系统电费最省为目标的室内温度值优化设定方法。通过优化后获得的室内温度设定值,可以得到空调系统的冷负荷需求。以一间典型的办公房间为例,通过构建一个带有热物质的简化模型,建立了工作期间和预冷期间的室内温度设定值与空调冷负荷的热平衡方程；通过分析建筑热物质模型中各参数对冷负荷的影响,建立了以空调系统电费最省为目标,舒适度指标为约束,室内温度设定值和预冷时间为决策变量的优化模型。实验结果表明在两阶段分时电价下,利用建筑热物质的蓄热功能,提前两小时采用预冷控制策略,相对于没有采用预冷策略的空调系统,在保证室内舒适度的前提下,可以节省9%的空调电费。优化后的分时电价室内冷负荷与基线冷负荷相比,削减了25.5%。由此可见,采用分时电价机制可以起到移峰填谷的作用。
4、针对风机盘管空调水泵控制常采用固定最不利支路位置和固定压差的问题,提出一种最不利支路位置和压差可变的优化方法。首先,从水力失调度的定义出发,以固定电价下得到的室内冷负荷需求为前提,提出了一种根据水力失调度来确定最不利支路位置的判定方法；然后,根据判定的最不利支路位置,对水系统进行了系统流量调节；最后,在固定电价下,以水泵能量输入比最小为目标函数,对各管路阻力特性系数和水泵扬程进行了优化控制；实验结果表明该优化方法相比于末端压差控制方法而言,水泵能耗不仅降低了20%左右,而且还能改善水力失调的现象。
5、针对变风量空调系统,从空调系统传输转换效率的角度出发,以第二、三章得到的空调冷负荷需求为基础,提出一种基于系统能量输入比的空调系统优化模型。首先根据空调系统各设备的特性,建立了各设备功率的性能曲线模型,并根据各设备的性能曲线模型,建立了空调系统能耗的性能曲线模型；然后根据空调系统的能量守恒约束、各设备的性能约束和各设备之间的耦合约束建立了空调系统的约束模型；最后对模型参数进行了简化,以空调系统能量输入比最小为目标,建立了空调系统各设备输出设定值优化模型；实验结果表明,该方法与以系统能耗比最小为目标的优化方法相比,固定电价下的系统功率提高了6.89%,分时电价下的系统功率提高了7.28%。因此,该方法具有更好的节能效果。
本文最后在总结全文工作的基础上,指出了不同电价机制下的办公建筑空调系统优化方法有待进一步研究的问题和方向。
The air conditioning user and electric energy provider are challenged continuously by the sustainable growth of the buiding's energy consumption and power demand. The low energy efficiency and serious energy waste is a universal phenomenon for the air conditioning system of office buildings, therefore, the main research purpose of this thesis is to realize the win-win results for the air conditioning user and electric energy provider by optimizing the energy consumption of air conditioning system under different price mechanism. The energy utilization efficiency is obtained by the multiplication of conversion efficiency, transmission efficiency and use efficiency of terminal energy. The improvement for the use efficiency of terminal energy can decrease greatly energy consumption in energy conversion and transmission chain. However, the actual energy analysis of air conditioning system is often focused on transimission chain.Therefore, it is essential to find a correct analysis approach which can begin from the analysis of terminal demand for air conditionding, then backward analysis for the transmission chain. To studing the cool load demand under different price mechanism is an effective method for improving use efficiency of air conditioningsystem. Based on system enegy input ratio, optimizing the transmission chain of fan coil and VAV system is an important means for improving the transmission efficiency of air conditioning system.
The main research of this theis is that the air conditioning syetem of office buildings is optimized under different price mechanism. The corresponding details are shown as follows:
1. Basing on the fact that the baseline cool load for air conditioning system is hard to predict effectively under different price mechanism, a kind of BP neural networks forecasting model based on FCM optimization preprocesses is proposed. The hourly outdoor meteorological data of daily demand response predicting is clustered by adopting the FCM method. The hourly load forecasting model is obstained by training network for a large of training samples based on BP neural network. The prediction results show that the prediction precision of the model is higher than that of linearity model, especially at the great variation moment of hour load.
2. A dynamic method based on optimal indoor temperature set point is proposed to solve problem of fixed indoor temperature set point under fixed power price. The minimal cool load demand is obtained by optimizing indoor temperature set point. Firstly, taking a simplified office as an example, an office building model with thermal mass is constructed. According to this model, the heat balance equation about cool load with indoor temperature set point and outdoor hourly tempareture is built. Then, the relation model about PMV with indoor temperature and thermal mass inner surface temperature is built by simplified PMV. Finally, taking the least cool load demand as an objective function, PMV as constraint, the optimal model on indoor temperature set point is built. The experimental results indicate that the thermal mass can delay the creation time of cool load peak value and have the effect of peak load shifting. Compared with the26℃temperature set point, the dynamic temperature can reduce the cool load demand by7percents as well as improve the PMV index. Compared with the baseline cool load, the cool load reduces by11.2percents under fixed price.
3. An optimal control method for indoor temperature set point based on minimal electricity charge is proposed to solve the problem of being hard to forecast cool load when precooling control strategy is used under TOU price. The terminal energy of the air conditioning system is obtained by optimizing indoor temperature set point. Taking a simplified office as an example, an office building model with thermal mass is constructed. Basing on this model, the heat balance equation about indoor temperature set point and cool load is built during work and precooling. It is analyzed that the parameters of thermal mass influence on cool load. Taking the least electricity charge as an objective function, PMV as constraint, and indoor temperature set point and precooling time as decision variable, the optimal model is built. Comparing with no adoption of precooling stratege, the experimental results indicate that using the precooling control strategy ahead of two hours based on two-stage TOU power price can reduce the electricity charge by9percents, as well as ensure the unchanged PMV index. Compared with the baseline cool load, the cool load reduces by by25.5percents under TOU price. Therefore, it can realize peak load shifting for adoping the TOU price.
4. An optimal method of dynamic position and changed pressure difference for the fan coil pump is proposed to solve problem of fixing most disadvantaged position and pressure difference.Firstly, starting from the difinition of the water maladjustment degree, a new judgment method was obtained to ensure the most disadvantaged branch position with the water maladjustment degree. Then, according to the judgment method, the system flow is regulated for the water system based on the cool load demand under fixed power price. Finally, taking the least energy input ratio of pump as an objective function, the resistance characteristic coefficient and pump lift is optimized under fixed power price. The experimental results indicate that compared with end pressure control method, the optimal method can reduce the pump consumption by20percents as well as improve the hydraulic aberration of the coupling pipe network.
5. Thinking of transmission chain of VAV of office buildings the air conditioning optimal model based on system energy input ratio is proposed to meet cool load demand under different price mechanism. Firstly, according to each equipment characteristic, the performance curve model of each equipment power is built. Based on these models, the performance curve model of system energy consumption is built too.Then, basing on energy conservation constraint, each equipment performance constraint and coupling constraint, the constraint equation is built. Finally, simplifying the model parameter, taking the least energy input ratio as an objective function, the optimal set point model of various equipments is built. The experimental results indicate that compared with the least energy consumption optimal method, the ststem power is improved by6.89percents under fixed power price, by7.28percents under TOU power price. Therefore, this method has better energy saving effect.
Finally, we point out a few existing problems to be solved after summarizing the works done in this dissertation.
本文对不同电价机制下的办公建筑空调系统的优化方法进行了深入的研究,主要的研究工作概括如下：
1、针对在不同电价机制下,空调系统基线冷负荷难以预测的问题,提出一种基于模糊C-均值聚类预处理的神经网络的空调系统冷负荷基线预测方法。采用模糊C-均值聚类的方法对预测日各时刻的室外气象数据进行了聚类；在此基础上,采用BP神经网络对大量的训练样本进行了网络训练,得到了各时刻的负荷预测模型；实验结果表明该预测模型整体预测精度都高于线性模型,对于小时负荷变化较大的时刻,更是显示了该模型的优越性。
2、针对在固定电价下,空调系统室内温度设定值常采用固定设定值的问题,提出一种根据室外逐时温度动态设定室内温度值的方法。通过获得的动态室内温度设定值,可以得到空调系统的最小冷负荷需求。首先,以一间典型的办公房间为例,通过构建一个带有热物质的简化模型,建立了空调冷负荷与室内温度设定值和室外逐时温度的热平衡方程；然后,对舒适度指标公式进行了简化,建立了舒适度指标与室内温度设定值和热物质内表面温度的关系模型；最后,基于上述模型,建立了以空调系统冷负荷需求最小为目标,舒适度指标为约束,室内温度设定值为决策变量的优化模型。实验结果表明建筑热物质能够延迟冷负荷峰值的产生时间,具有削峰填谷的作用。动态温度设定值相比于26℃固定温度设定值,不仅空调冷负荷需求减少了7%,而且还能改善PMV的指标。优化后的固定电价室内冷负荷与基线冷负荷相比,削减了11.2%。
3、针对在分时电价下,采用预冷控制策略时,室内冷负荷需求难以预测的问题,提出一种以空调系统电费最省为目标的室内温度值优化设定方法。通过优化后获得的室内温度设定值,可以得到空调系统的冷负荷需求。以一间典型的办公房间为例,通过构建一个带有热物质的简化模型,建立了工作期间和预冷期间的室内温度设定值与空调冷负荷的热平衡方程；通过分析建筑热物质模型中各参数对冷负荷的影响,建立了以空调系统电费最省为目标,舒适度指标为约束,室内温度设定值和预冷时间为决策变量的优化模型。实验结果表明在两阶段分时电价下,利用建筑热物质的蓄热功能,提前两小时采用预冷控制策略,相对于没有采用预冷策略的空调系统,在保证室内舒适度的前提下,可以节省9%的空调电费。优化后的分时电价室内冷负荷与基线冷负荷相比,削减了25.5%。由此可见,采用分时电价机制可以起到移峰填谷的作用。
4、针对风机盘管空调水泵控制常采用固定最不利支路位置和固定压差的问题,提出一种最不利支路位置和压差可变的优化方法。首先,从水力失调度的定义出发,以固定电价下得到的室内冷负荷需求为前提,提出了一种根据水力失调度来确定最不利支路位置的判定方法；然后,根据判定的最不利支路位置,对水系统进行了系统流量调节；最后,在固定电价下,以水泵能量输入比最小为目标函数,对各管路阻力特性系数和水泵扬程进行了优化控制；实验结果表明该优化方法相比于末端压差控制方法而言,水泵能耗不仅降低了20%左右,而且还能改善水力失调的现象。
5、针对变风量空调系统,从空调系统传输转换效率的角度出发,以第二、三章得到的空调冷负荷需求为基础,提出一种基于系统能量输入比的空调系统优化模型。首先根据空调系统各设备的特性,建立了各设备功率的性能曲线模型,并根据各设备的性能曲线模型,建立了空调系统能耗的性能曲线模型；然后根据空调系统的能量守恒约束、各设备的性能约束和各设备之间的耦合约束建立了空调系统的约束模型；最后对模型参数进行了简化,以空调系统能量输入比最小为目标,建立了空调系统各设备输出设定值优化模型；实验结果表明,该方法与以系统能耗比最小为目标的优化方法相比,固定电价下的系统功率提高了6.89%,分时电价下的系统功率提高了7.28%。因此,该方法具有更好的节能效果。
本文最后在总结全文工作的基础上,指出了不同电价机制下的办公建筑空调系统优化方法有待进一步研究的问题和方向。
The air conditioning user and electric energy provider are challenged continuously by the sustainable growth of the buiding's energy consumption and power demand. The low energy efficiency and serious energy waste is a universal phenomenon for the air conditioning system of office buildings, therefore, the main research purpose of this thesis is to realize the win-win results for the air conditioning user and electric energy provider by optimizing the energy consumption of air conditioning system under different price mechanism. The energy utilization efficiency is obtained by the multiplication of conversion efficiency, transmission efficiency and use efficiency of terminal energy. The improvement for the use efficiency of terminal energy can decrease greatly energy consumption in energy conversion and transmission chain. However, the actual energy analysis of air conditioning system is often focused on transimission chain.Therefore, it is essential to find a correct analysis approach which can begin from the analysis of terminal demand for air conditionding, then backward analysis for the transmission chain. To studing the cool load demand under different price mechanism is an effective method for improving use efficiency of air conditioningsystem. Based on system enegy input ratio, optimizing the transmission chain of fan coil and VAV system is an important means for improving the transmission efficiency of air conditioning system.
The main research of this theis is that the air conditioning syetem of office buildings is optimized under different price mechanism. The corresponding details are shown as follows:
1. Basing on the fact that the baseline cool load for air conditioning system is hard to predict effectively under different price mechanism, a kind of BP neural networks forecasting model based on FCM optimization preprocesses is proposed. The hourly outdoor meteorological data of daily demand response predicting is clustered by adopting the FCM method. The hourly load forecasting model is obstained by training network for a large of training samples based on BP neural network. The prediction results show that the prediction precision of the model is higher than that of linearity model, especially at the great variation moment of hour load.
2. A dynamic method based on optimal indoor temperature set point is proposed to solve problem of fixed indoor temperature set point under fixed power price. The minimal cool load demand is obtained by optimizing indoor temperature set point. Firstly, taking a simplified office as an example, an office building model with thermal mass is constructed. According to this model, the heat balance equation about cool load with indoor temperature set point and outdoor hourly tempareture is built. Then, the relation model about PMV with indoor temperature and thermal mass inner surface temperature is built by simplified PMV. Finally, taking the least cool load demand as an objective function, PMV as constraint, the optimal model on indoor temperature set point is built. The experimental results indicate that the thermal mass can delay the creation time of cool load peak value and have the effect of peak load shifting. Compared with the26℃temperature set point, the dynamic temperature can reduce the cool load demand by7percents as well as improve the PMV index. Compared with the baseline cool load, the cool load reduces by11.2percents under fixed price.
3. An optimal control method for indoor temperature set point based on minimal electricity charge is proposed to solve the problem of being hard to forecast cool load when precooling control strategy is used under TOU price. The terminal energy of the air conditioning system is obtained by optimizing indoor temperature set point. Taking a simplified office as an example, an office building model with thermal mass is constructed. Basing on this model, the heat balance equation about indoor temperature set point and cool load is built during work and precooling. It is analyzed that the parameters of thermal mass influence on cool load. Taking the least electricity charge as an objective function, PMV as constraint, and indoor temperature set point and precooling time as decision variable, the optimal model is built. Comparing with no adoption of precooling stratege, the experimental results indicate that using the precooling control strategy ahead of two hours based on two-stage TOU power price can reduce the electricity charge by9percents, as well as ensure the unchanged PMV index. Compared with the baseline cool load, the cool load reduces by by25.5percents under TOU price. Therefore, it can realize peak load shifting for adoping the TOU price.
4. An optimal method of dynamic position and changed pressure difference for the fan coil pump is proposed to solve problem of fixing most disadvantaged position and pressure difference.Firstly, starting from the difinition of the water maladjustment degree, a new judgment method was obtained to ensure the most disadvantaged branch position with the water maladjustment degree. Then, according to the judgment method, the system flow is regulated for the water system based on the cool load demand under fixed power price. Finally, taking the least energy input ratio of pump as an objective function, the resistance characteristic coefficient and pump lift is optimized under fixed power price. The experimental results indicate that compared with end pressure control method, the optimal method can reduce the pump consumption by20percents as well as improve the hydraulic aberration of the coupling pipe network.
5. Thinking of transmission chain of VAV of office buildings the air conditioning optimal model based on system energy input ratio is proposed to meet cool load demand under different price mechanism. Firstly, according to each equipment characteristic, the performance curve model of each equipment power is built. Based on these models, the performance curve model of system energy consumption is built too.Then, basing on energy conservation constraint, each equipment performance constraint and coupling constraint, the constraint equation is built. Finally, simplifying the model parameter, taking the least energy input ratio as an objective function, the optimal set point model of various equipments is built. The experimental results indicate that compared with the least energy consumption optimal method, the ststem power is improved by6.89percents under fixed power price, by7.28percents under TOU power price. Therefore, this method has better energy saving effect.
Finally, we point out a few existing problems to be solved after summarizing the works done in this dissertation.
引文
[1]清华大学建筑节能研究中心.中国建筑节能年度发展研究报告2009[M].北京：中国建筑工业出版社,2009.
[2]J. R.Waldram.The theory of thermodynamics [M].New York:Cambridge University Press, 1985.
[3]J.E. Braun. Methodologies for design and control of central cooling plants [D]. Department of Mechanical Engineering, University of Wisconsin,1988.
[4]J.E.Braun, S.A.Klein, J.W.Mitchell. Effectiveness models for cooling towers and cooling coils [J]. ASHRAE Trans,1989,95(2):164-174.
[5]J.E.Braun, Diderrich. Near Optimal Control of Cooling Towers for Chilled Water Systems [J]. ASHRAE Trans,1990:806-813.
[6]S.V.Shelton, C.T.Joyce. Cooling tower optimization for centrifugal chillers [J]. ASHRAE J, 1991,33(6):28-36.
[7]M.J.Brandemuehl. A toolkit for secondary HVAC system energy calculations [M]. Atlanta: ASHRAE; 1993.
[8]M.A.Bernier. Cooling tower performance:theory and experiments [J]. ASHRAE Trans,1994, 100(2):114-121.
[9]A.B.Michel. Thermal performance of cooling towers [J]. ASHRAE J,1995,37(4):56-61.
[10]J. Lebrun. A toolkit for primary HVAC system energy calculations [M]. Atlanta:ASHRAE; 1999.
[11]R.J. Rabehl, J.W. Mitchell, W.A. Beckman. Parameter estimation and the use of catalog data in modeling heat exchangers and coils [J]. HVAC&R Research,1999,5(1):3-17.
[12]M.S.Soylemez.Theoretical and experimental analyses of cooling towers [J]. ASHRAE Trans, 1999,105(1):330-337.
[13]A.Hasan, K.Siren. Theoretical and computational analysis of closed wet cooling towers and its applications in cooling of buildings [J]. Energy Build,2002,34:477-486.
[14]Y.W.Wang, W.J.Cai, Y.C.Soh, et al. A simplified modeling of cooling coils for control and optimization of HVAC systems [J]. Energy conversation and Management,2004,45: 2915-2930.
[15]G.Y. Jin, W.J.Cai, Y.W.Wang, et al. A simple dynamic model of cooling coil unit [J]. Energy conversation and Management,2006,47:2659-2672.
[16]L.Lu, W.J.Cai, Y. C. Soh, et al. HVAC system optimization--condenser water loop [J]. Energy conversation and Management,2004,45:613-630.
[17]W.Z.Huang, M. Zaheeruddin, S.H. Cho. Dynamic simulation of energy management control functions for HVAC systems in buildings [J]. Energy conversation and Management,2006, 47:926-943.
[18]B.Tashtoush, M. Molhim, M. Al-Rousan. Dynamic model of an HVAC system for control analysis [J]. Energy,2005,30:1729-1745.
[19]Z.J.Ma, S.W.Wang, X.H.Xu, et al. A supervisory control strategy for building cooling water systems for practical and real time applications [J]. Energy Conversion and Management 2008; 49(8):2324-36.
[20]Z.J.Ma, S.W.Wang. An optimal control strategy for complex building central chilled water systems for practical and real-time applications [J]. Building and Environment,2009,44: 1188-1198.
[21]G. P. Henze, B.Biffar, D.Kohn, et al. Optimal design and operation of a thermal storage system for a chilled water plant serving pharmaceutical buildings [J]. Energy and Buildings,2008,40:1004-1019.
[22]C.Furlong. Optimizing Chillers and Towers [J]. ASHRAE,2004,46(7):123-129.
[23]F.W.Yu, K.T.Chan. Optimization of water-cooled chiller system with load-based speed control [J]. Energy Conversion and Management, Applied Energy,2008,85:931-950
[24]Y.J.Sun, S. W.Wang, G.S.Huang. Chiller sequencing control with enhanced robustness for energy efficient operation [J]. Energy and Buildings,2009,41:1246-1255.
[25]S.W.Wang, X.Q.Jin.Model-based optimal control of VAV air-conditioning system using genetic algorithm [J]. Buiding and Environment,2000,35(6):471-487.
[26]N.Nassif, S.Kajl, R.Sabourin. Simplified model-based optimal control of VAV air conditioning system [J]. Building Simulation,2005:823-830.
[27]赵相彬,赵哲身,孙怡佳.VAV空调系统的变静压控制和研究方法[J].微计算机信息,2005,21(6)：17-19.
[28]N.Nassif, S.Moujaes. A cost-effective operating strategy to reduce energy consumption in a HVAC system [J]. International Journal of Energy Research,2008,32:543-558.
[29]T.Y.Chen. Real-time predictive supervisory operation of building thermal systems with thermal mass [J]. Energy and Buildings,2001,33:141-150.
[30]L.Lu, W.J.Cai, Y.C.Soh, et al. Global optimization for overall HVAC systems--Part Ⅰ problem formulation and analysis [J]. Energy Conversion and Management,2005,46: 999-1014.
[31]L.Lu, W.J.Cai, Y.C.Soh, et al.Global optimization for overall HVAC systems-Part Ⅱ problem solution and simulations [J]. Energy Conversion and Management,2005,46: 1015-1028.
[32]G.R.ZHENG, M.Zaheer-uddin.Optimization of thermal processes in a variabal air volume HVAC system [J]. Energy,1996,21(5):407-420.
[33]W.Z.Huang, M.Zaheer-uddin, S.H.Cho. Dynamic simulation of energy management control functions for HVAC systems in buildings [J]. Energy Conversion and Management,2006, 47:926-943.
[34]A.Kusiak, M.Y. Li, F.Tang. Modeling and optimization of HVAC energy consumption [J], Applied Energy,2010,87:3092-3102.
[35]GJahedi, M.M.Ardehali. Wavelet based artificial neural network applied for energy efficiency enhancement of decoupled HVAC system [J]. Energy Conversion and Management,2012,54:47-56.
[36]M.A.Piette, S.Kiliccote.Characterization and Demonstration of Demand Responsive Control Technologies and Strategies in Commercial Buildings[R].California:LBNL, Tech Rep, 2006:1-23.
[37]L.K.Rawlings. Ice storage system optimization and control strategies [J]. ASHRAE Transactions,1985,91 (2):12-23.
[38]J.E.Braun. A comparison of chiller priority, storage priority, and optimal control of an ice storage system [J]. ASHRAE Transactions,1992,98(1):893-902.
[39]何大四,张旭.蓄冷系统新型节能优化运行控制方法[J].建筑科学,2007,23(2)：27-30.
[40]李先庭,李寒春,石文星.基于设计周期负荷的冰蓄冷系统设计[J].暖通空调,2007,37(5)：83-88.
[41]J.E.Braun.Load control using building thermal mass [J].Journal of solar energy engineering-transactions of the ASME,2003,125(3):292-301.
[42]P. Xu, P. Haves. Case study of demand shifting with thermal mass in two large commercial buildings [J]. ASHRAE Transactions 2006,2006,112(1):572-580.
[43]J. Roth, J. Dieckmann, J. Brodrick. Using Off-Peak Precooling [J]. ASHRAE JOURNAL, 2009,51(3):80-83.
[44]S.Morgan, M.Krarti. Impact of electricity rate structures on energy cost savings of pre-cooling controls for office buildings [J]. Building and Environment,2007,42(8): 2810-2818.
[45]K.Srinivas, N.Lu. Evaluation of Residential HVAC Control Strategies for Demand Response Programs [J]. ASHRAE Trans,2006,112(2):535-546.
[46]J.R.Ma, J.Qin, T.Salsbury, et al. Demand reduction in building energy systems based on economic model predictive control [J]. Chemical Engineering Science,2012 67:92-100.
[47]蔡文剑,贾磊,王雷,杜晓通.建筑节能技术与工程基础[M].北京：机械工业出版社,2007.
[48]K.Coughlin, M.A.Piette, C.Goldman, et al. Estimating demand response load impact: evaluation of baseline load models for non-residential buildings in [R], California: LBNL, Tech Rep,2008,1-28.
[49]K.Coughlin, M.A.Piette, C.Goldman, et al. Statistical analysis of baseline load models for non-residential buildings [J]. Building and Environment,2009,41:374-381.
[50]S.J.Inkinen, Y. Neuvo. Base line estimation using sparse nonlinear operators [J]. Signal Processing,2002,82:811-817.
[51]S.Bukvic, D.Spasojevic. An alternative approach to spectrum base line estimation [J]. Spectrochimica Acta Part B,2005,60:1308-1315.
[52]D. Reza, A. Saeed. Demand response regulation modeling based on distribution system asset efficiency [J]. Electric Power Systems Research,2011,81:667-676.
[53]J.H.Wang, N.B.Cary, Z.G.Hu, et al. Demand response in China [J]. Energy,2010, 35:1592-1597.
[54]O.Sezgena, C.A.Goldmana, P.Krishnaraob. Option value of electricity demand response [J]. Energy,2007,32:108-119.
[55]P.Cappers, C.Goldman, D.Kathan. Demand response in U.S. electricity markets:Empirical evidence [J]. Energy,2010,35:1526-1535.
[56]M. H Albadi, E.F.Eisaadany. A summary of demand response in electricity markets [J]. Electric Power Systems Research,2008,78:1989-1996.
[57]J.H.KIM, A.Shcherbakova. Common failures of demand response [J]. Energy,2011,36: 873-880.
[58]C.I.Hsu, Y.H.Wen. Reliability evaluation for airline network design in response to fluctuation in passenger demand [J]. Omega,2002,30:197-213.
[59]R. AAZAMI, K.AFLAKI, M.R.HAGHIFAM. A demand response based solution for LMP management in power markets [J]. Electrical Power and Energy Systems,2011,11:1-8.
[60]崔航,王冕,罗贵明,等.基于扩展时序距离法和自适应控制的超短期负荷预测[J].电力系统自动化,2009,33(15)：38-42.
[61]黎灿兵,李晓辉,赵瑞,等.电力短期负荷预测相似日选取算法[J].电力系统自动化,2008,32(9)：69-73.
[62]畅广辉,刘涤尘,熊浩.基于多分辨率SVM回归估计的短期负荷预测[J].电力系统自动化,2007,31(9)：37-41.
[63]何大四,张旭.改进的季节性指数平滑法预测空调负荷分析[J].同济大学学报：自然科学版,2005,33(12)：1672-1676.
[64]万迪光.不确定输入对电力负荷预测的影响[J].山东大学学报：工学版,2004,34(5)：55-58.
[65]高荣,刘晓华.基于小波变换的支持向量机短期负荷预测[J].山东大学学报：工学版,2005,35(3)：115-118.
[66]D.M.Tsai, C.C.Lin. Fuzzy C-means based clustering for linearly and nonlinearly separable data Pattern Recognition [J].2011,44:1750-1760
[67]徐巍,陈祥光,彭红星,等.基于模糊C-均值聚类与支持向量机的PMV指标预测系统[J].系统工程理论与实践,2009,29(7)：119-124.
[68]王旭平,王汉功,陈小虎.基于模糊C均值算法的齿轮泵故障诊断技术[J].机床与液压,2009,37(10)：269-272.
[69]J.W.Liu, M.Z.Xu.Kernelized fuzzy attribute C-means clustering algorithm Fuzzy Sets and Systems [J] Fuzzy Sets and Systems,2008,159:2428-2445.
[70]J.C.Bezdek, R.Ehrlich, W.Full. FCM:The fuzzy C-means clustering algorithm [J] Computers & Geosciences,1984,10:191-203.
[71]胡守仁.神经网络导论[M].北京：国防科技大学出版社,1993.
[72]王伟.人工神经网络原理——入门与应用[M].北京：北京航空航天大学出版社,1995.
[73]刘伟.采用负荷预测的冰蓄冷空调系统[D].西安：西安建筑科技大学,2005.
[74]A.P.Plumb, C.R.Rowe, P.York, et al. Optimisation of the predictive ability of artificial neural network (ANN) models:A comparison of three ANN programs and four classes of training algorithm [J]. European Journal of Pharmaceutical Sciences,2005,25:395-405.
|75] R.Battiti. First- and second-order methods for learning:between steepest descent and Newton's method [J].Neural Comput,1992,4:141-166.
[76]C.Q. Li, S.L. Guo, J. Zhang. Modified NLPM-ANN model and its application [J]. Journal of Hydrology,2009,378:137-141.
[77]B.Pang, S.L.Guo, L.H Xiong, et al.2007. A nonlinear perturbation model based on artificial neural network [J]. Journal of Hydrology,2007,333:504-516.
[78]马庆,李歧强,聂清珍.公共建筑空调温度设定值的动态优化控制研究[J].系统工程学报,2011,26(4)：435-441.
[79]C.A.Balaras. The role of thermal mass on the cooling load of buildings. An overview of computational methods [J]. Energy and Buildings,1996,24:1-10.
[80]E.H.Mathews, P.G.Rousseau, P.G.Richards, et al. A procedure to estimate the effective heat storage capability of a building [J].Building and Environment,1991,26(2):179-188.
[81]L.Zhu, R.Hurt, D.Correia, et al. Detailed energy saving performance analyses on thermal mass walls demonstrated in a zero energy house [J]. Energy and Buildings,2009,41: 303-310.
[82]K.J.Kontoleon, D.K. Bikas.The effect of south wall's outdoor absorption coefficient on time lag, decrement factor and temperature variations [J]. Energy and Buildings,2007,39 (9):1011-1018.
[83]K.Gregory, B.Moghtaderi, H.Sugo, et al. Effects of thermalmass on the thermal performance of various Australian residential constructions systems [J]. Energy and Buildings,2008,40 (4):459-465.
[84]P.Burberry.Practical Thermal Design in Buildings [M], London:Batsford Academic and Educational Ltd.,1983.
[85]B. Givoni. Man. Climate and Architecture,2nd ed. [M]. London:Applied Science,1981.
[86]J.L.Zhou, G.Q.Zhang, Y.L.Lin, et al. Energy analysis of buildings employing thermal mass in Cyprus [J].Energy and Buildings,2008,40 (6):979-986.
[87]J.N. Hacker, T.P. De Saulles, A.J. Minson, et al. Embodied and operational carbon dioxide emissions from housing:a case study on the effects of thermal mass and climate change [J], Energy and Buildings,2008,40 (3):375-384.
[88]E. Shaviv, A. Yezioro, I. Capeluto. Thermal mass and night ventilation as passive cooling design strategy [J].Renewable Energy,2001,24 (3/4):445-452.
[89]M. Brown. Optimization of thermal mass in commercial building applications, Journal of Solar Energy Engineering [J]. Transactions of the ASME,1990,112 (4):273-279.
[90]T. Kusuda, J. Bean. Comparison of calculated hourly cooling load and indoor temperature with data for high mass building tested in an environmental chamber [J]. ASHRAE Transactions,1981,87:1232-1240.
[91]D. Burch, S. Malcolm, K. Davis. The effect of wall mass on the summer space cooling of six test buildings [J]. ASHRAE Transactions,1984,90:5-21.
[92]J.E.Braun. Reducing energy costs and peak electrical demand through optimal control of building thermal storage [J].ASHRAE Transactions,1990,96(2):876-888.
[93]L.N.Yang, Y.G.Li. cooling load reduction by using thermal mass and night ventilation [JJ.Energy and Buildings,2008,40:2052-2058.
[94]M.Kawashima, C.E.Dorgan, J.W.Mitchell. Hourly Thermal Load Prediction for the Next 24 Hours by ARIMA, EWMA, LR, and an Artificial Neural Network [J]. ASHRAE Transactions,1995,101(1):186-200
[95]J.W.MacArthur. On-line recursive estimation for load profile prediction [J].ASHRAE Transactions,1989,95(1):621-628.
[96]王勇.冰蓄冷系统优化控制技术的研究[D].清华大学,1997.
[97]M.Kawashima, C.E.Dorgan, J.W.Mitchell. Hourly Thermal Load Prediction for the Next 24 Hours by ARIMA, EWMA, LR, and an Artificial Neural Network [J]. ASHRAE Transactions,1995,101(1):186-200
[98]T.Y.Chen, A.K.Athienitis. Ambient temperature and solar radiation prediction for predictive control of HVAC systems and a methodology for optimal building heating dynamic operation [J]. ASHRAE Transactions,1996,102(1):26-36.
[99]F.C.麦奎斯顿,J.D.帕克,J.D.斯皮特勒.空调工程[M].俞炳丰.北京：化学工业出版社,2005.
[100]申欢迎,秦萍,董军.环境因素对PMV指标的影响分析[J].制冷与空调,2003,(4),27-29.
[101]叶海,魏润柏.热环境客观评价的一种简易方法[J].人类工效学,2004,10(3)：16-19.
[102]K.H.Lee, J.E.Braun. Evaluation of methods for determining demand-limiting set point trajectories in buildings using short-term measurements [J]. Building and Environment, 2008,43(10):1769-1783.
[103]K.H.Lee, J.E.Braun. Development of methods for determining demand-limiting set point trajectories in buildings using short-term measurements [J]. Building and Environment, 2008,43(10):1755-1768.
[104]K.H.Lee, J.E.Braun. Model-based demand-limiting control of building thermal mass [J]. Building and Environment,2008,43(10):1633-1646.
[105]A. Rabl, L.K. Norford. Peak load reduction by preconditioning buildings at night [J], International Journal of Energy Research,1991,15:781-798.
[106]N.Zhu, S.W.Wang, Z.J.Ma, et al. Energy performance and optimal control of air-conditioned buildings with envelopes enhanced by phase change materials [J], Energy Conversion and Management,2011,52:3197-3205.
[107]K.R. Keeney, J.E. Braun. Application of building precooling to reduce peak cooling requirements [J], ASHRAE Transactions,1997,103:463-469.
[108]P. Xu, P. Haves, J.E. Braun, et al.Peak demand reduction from pre-cooling with zone temperature reset in an office building [A], Proceedings of 2004 ACEEE Summer Study of Energy Efficiency in Buildings [C], Pacific Grove, CA,2004:1235-1239.
[109]R.X.Yin, P.Xu, M. A.Piette, et al. Study on Auto-DR and pre-cooling of commercial buildings with thermal mass [J]. Energy and Buildings,2010,42:967-975.
[110]胡钦华,李奎山,Albert T P So居民建筑夏季提高室内空调温度设定值的可行性研究[J].东莞理工学院学报,2008,15(3)：104-107.
[111]杜雪伟,王海斌,杜志敏,等.基于提前启动和建筑储能的VAV系统优化运行策略[J]. 建筑热能通风空调,2007,26(5)：7-10.
[112]李建兴,涂光备,涂岱昕.多泵并联水系统的技术经济分析[J].流体机械,2004,32(10)：38-41.
[113]李彬,肖勇全,李德英,等.变流量空调水系统的节能探讨[J].暖通空调,2006,36(1)：132-136.
[114]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建[J].建筑科学,2007,23(6)：12-15.
[115]李苏泷,邹娜.空调冷却水变流量控制方法研究[J].暖通空调,2005,35(12)：51-53.
[116]刘亮,孙育英,杨超.二次泵变流量水系统的设计[J].暖通空调,2007,37(9)：110-115.
[117]罗新梅,周向阳,曾祖铭.水泵变流量运行性能分析[J].华东交通大学学报,2004,21(4)：19-21.
[118]孟彬彬,朱颖心,林波荣.部分负荷下一次泵水系统变流量性能研究[J].2002,32(6)：108-110.
[119]符永正.水泵并联运行的流量增量及相关问题分析[J].暖通空调,2004,34(11)：83-85.
[120]董宝春,刘传聚,杨伟.变流量水系统优化运行的探讨[J].暖通空调,2007,37(1)：55-59.
[121]董宝春,刘传聚,刘东,等.一次泵/二次泵变流量系统能耗分析[J].暖通空调,2005,35(7)：82-85.
[122]T.S.Lee, K.Y.Liao, W.C.Lu. Evaluation of the suitability of empirically-based models for predicting energy performance of centrifugal water chillers with variable chilled water flow [J]. Applied Energy,2012,93:583-595.
[123]S.W.Wang, Z.J.Ma, D.C.Gao. Performance enhancement of a complex chilled water system using a check valve:Experimental validation [J]. Applied Thermal Engineering, 2010,30:2827-2832.
[124]Z.J.Ma, S.W.Wang. An optimal control strategy for complex building central chilled water systems for practical and real-time applications [J]. Building and Environment,2009, 44:1188-1198.
[125]F.W. Yu, K.T. Chan. Improved energy performance of air cooled centrifugal chillers with variable chilled water flow [J]. Energy Conversion and Management,2008,49:1595-1611.
[126]Hartman T. All-variable speed centrifugal chiller plants [J]. ASHRAE J,2001,43(9):43-53.
[127]I. Dubov. Chilled water plant effciency [J]. ASHRAE J,2003,45(6):37-40.
[128]Saththasivam J, Ng KC. Predictive and diagnostic methods for centrifugal chillers [J]. ASHRAE Trans,2008,114(1):282-7.
[129]T.Moses. Variable-primary flow:important lessons learned [J]. Heating/Piping/Air Condition Eng 2004,76(7):40-43.
[130]M.Liu. Variable water flow pumping for central chilled water systems [J].J Solar Energy Engineering 2002,124(3):300-304.
[131]X.F.Liu, J.P.Liu, J.D.Lu, et al. Research on operating characteristics of direct-return chilled water system controlled by variable temperature difference [J]. Energy,2012,76:1-4.
[132]S.T.Taylor. Primary-only vs. primary secondary variable flow systems [J].ASHRAE J, 2002,44(2):25-9.
[133]J.E.Braun, S.A.Klein, J.W.Mitchell, et al. Applications of optimal control to chilled water systems without storage. ASHRAE Trans 1989,95(1):663-675.
[134]G.Avery. Improving the effciency of chilled water plants [J]. ASHRAE J,2001, 43(5):14-18.
[135]F.W.Yu, K.T.Chan. Optimum load sharing strategy for multiple-chiller systems serving air-conditioned buildings [J]. Build Environment,2007,42(4):1581-1593.
[136]K.A.Brownell, S.A.Klein, D.T. Reindl. Refrigeration system malfunctions [J]. ASHRAE J 1999,41:40-46.
[137]F.W. Yu, K.T.Chan. Tune up the set point of condensing temperature for more energy effcientair cooled chillers [J]. Energy Conversion Manage 2006,47:2499-2514.
[138]F.W.Yu, K.T.Chan. Life cycle analysis of enhanced condenser features for air-cooled chillers serving air-conditioned buildings [J]. Build Environment 2006,41:981-991.
[139]S.W.Wang. Dynamic simulation of a central chilling system and evaluation of EMCS on-line control strategies [J]. Build Environment,1998,33(1):1-20.
[140]M.J.Kempiak, R.R.Crawford. Three-zone steady-state modelling of a mobile air-conditioning condenser [J]. ASHRAE Trans 1992,98(1):475-488.
[141]F.W. Yu, K.T. Chan. Low-energy design for air-cooled chiller plants in air-conditioned buildings [J]. Energy Buildings,2006,38(4):334-339.
[142]T.S.Lee, W.C.Lu. An evaluation of empirically-based models for predicting energy performance of vapor-compression water chillers [J].Applied Energy,2010, 87(11):3486-93.
[143]涂岱昕,李建兴,胡振杰.空调变水量系统水泵变频的相关问题[J].流体机械,2007,35(1)：49-63.
[144]姚国梁.空调变频水泵节能问题探讨[J].暖通空调,2004,34(6)：32-34.
[145]毕崇宁.暖通空调水系统效率优化策略研究[D].山东大学,2008.
[146]杜文学.空调一级泵变流量水系统的管网优化[D].华中科技大学,2005.
[147]杨伟.区域供冷水系统节能运行研究[D].同济大学,2007.
[148]L.Lu, W.J.Cai, L.H.Xie, et al. HVAC system optimization—in-building section [J]. Energy and Buildings,2005(37):11-22.
[149]哈尔滨建筑工程大学.供热工程[M].北京：中国建筑工业出版社,1985.
[150]李建华,康相玖,陶海臣,等.变频控制在中央空调系统的节能可行性分析[J].制冷与空调,2003,3(1)：44-47.
[151]崔景潭,晏刚,周晋,等.变频空调压缩机及变频调速系统的技术现状[J].制冷与空调,2003,3(2)：11-18.
[152]P.L.Luis, O.Jose, R. M.Ismael. Constructing HVAC energy efficiency indicators [J]. Energy and Buildings,2012,47:619-629.
[153]P.L.Luis, O.Jose, R. M.Ismael. The map of energy flow in HVAC systems [J]. Applied Energy,2011,88:5020-5031.
[154]P.L.Luis, O.Jose, C. F. Juan, et al. A review of HVAC systems requirements in building energy regulations [J]. Energy and Buildings,2011,43:255-268.
[155]I.Korolijaa, L.M.Halburdb, Y.Zhang, et al. Influence of building parameters and HVAC systems coupling on building energy performance [J]. Energy and Buildings,2011,43: 1247-1253.
[156]A.P.Wemhoff, M.V. Frank. Predictions of energy savings in HVAC systems by lumped models [J]. Energy and Buildings,2010,42:1807-1814.
[157]N.N.Grace, S.A.Tassou. Dynamic simulation of liquid chillers [A], Proc International Ref rigeration Conference [C]. Purdue,2000:433-470.
[158]M.Ning, M.Zaheeruddin. Neuro-optimal operation of a variable air volume HVAC&R system [J]. Applied Thermal Engineering,2010,30:385-399
[159]葛云亭,彦启森,彭雄兵.制冷空调系统仿真数学模型的理论与实验研究[J].制冷学报,1995,4：9-18.
[160]M.T.Baltaa, I. Dincerb, A.Hepbasli. Performance and sustainability assessment of energy options for building HVAC applications [J]. Energy and Buildings,2010,42:1320-1328.
[161]K.F. Fong, V.I. Hanby, T.T. Chow. HVAC system optimization for energy management by evolutionary programming [J]. Energy and Buildings,2006,38:220-231.
[162]J.F. Montgomerya, S.I.Greena, S.N.Rogak, et al. Predicting the energy use and operation cost of HVAC air filters [J]. Energy and Buildings,2012,47:643-650.
[163]M.Fasiuddin, I. HVAC system strategies for energy conservation in commercial buildings in Saudi Arabia [J]. Energy and Buildings,2011,43: 3457-3466.
[164]E.E.Guillermo, S.H.Isidoro, A.O.Manuel. Application of an energy management and control system to assess the potential of different control strategies in HVAC systems [J]. Energy and Buildings,2010,42:2258-2267.
[165]A.P.Wemhoff. Application of optimization techniques on lumped HVAC models for energy conservation [J]. Energy and Buildings,2010,42:2445-2451.
[166]P.Bryan,Rasmussen,G. Andrew, et al. Control -oriented modeling of Trancritical vapor compression systems [J]. Journal of Dynamic Systems, Measurement, and Control,2004, 126(54):358-361.
[167]S.Soyguder. Intelligent system based on wavelet decomposition and neural network for predicting of fan speed for energy saving in HVAC system [J]. Energy and Buildings,2011, 43:814-822.
[168]B.Rasmussen, A.Alleyne, C.Bullard, et al. Control- oriented modeling and analysis of automotive transcritical AC system dynamics [A].2002 American Control Conference [C]. Anchorage, USA,2002 (4):3111-3116.
[169]A.P.Wemhoff. Calibration of HVAC equipment PID coefficients for energy conservation [J]. Energy and Buildings,2012,45:60-66.
[170]B.P.Rasmussen, A.Alleyne, A.Musser. Model-driven system identification of transcritical vapor compression systems [J]. IEEE Trans on Control Systems Technology,2005, 13(3):444-457.
[171]A.Kusiak, F.Tang, G.L.Xu. Multi-objective optimization of HVAC system with an evolutionary computation algorithm [J]. Energy,2011,36 (5):2440e2449.
[172]S.D.Antonellis, C.M.Joppolo, L.M.Vapor, et al. Simulation and energy efficiency analysis of desiccant wheel systems for drying processes [J]. Energy,2012,37 (1) 336-345.
[173]聂清珍.暖通空调系统节能与节支优化策略研究[D].山东大学,2010.
[2]J. R.Waldram.The theory of thermodynamics [M].New York:Cambridge University Press, 1985.
[3]J.E. Braun. Methodologies for design and control of central cooling plants [D]. Department of Mechanical Engineering, University of Wisconsin,1988.
[4]J.E.Braun, S.A.Klein, J.W.Mitchell. Effectiveness models for cooling towers and cooling coils [J]. ASHRAE Trans,1989,95(2):164-174.
[5]J.E.Braun, Diderrich. Near Optimal Control of Cooling Towers for Chilled Water Systems [J]. ASHRAE Trans,1990:806-813.
[6]S.V.Shelton, C.T.Joyce. Cooling tower optimization for centrifugal chillers [J]. ASHRAE J, 1991,33(6):28-36.
[7]M.J.Brandemuehl. A toolkit for secondary HVAC system energy calculations [M]. Atlanta: ASHRAE; 1993.
[8]M.A.Bernier. Cooling tower performance:theory and experiments [J]. ASHRAE Trans,1994, 100(2):114-121.
[9]A.B.Michel. Thermal performance of cooling towers [J]. ASHRAE J,1995,37(4):56-61.
[10]J. Lebrun. A toolkit for primary HVAC system energy calculations [M]. Atlanta:ASHRAE; 1999.
[11]R.J. Rabehl, J.W. Mitchell, W.A. Beckman. Parameter estimation and the use of catalog data in modeling heat exchangers and coils [J]. HVAC&R Research,1999,5(1):3-17.
[12]M.S.Soylemez.Theoretical and experimental analyses of cooling towers [J]. ASHRAE Trans, 1999,105(1):330-337.
[13]A.Hasan, K.Siren. Theoretical and computational analysis of closed wet cooling towers and its applications in cooling of buildings [J]. Energy Build,2002,34:477-486.
[14]Y.W.Wang, W.J.Cai, Y.C.Soh, et al. A simplified modeling of cooling coils for control and optimization of HVAC systems [J]. Energy conversation and Management,2004,45: 2915-2930.
[15]G.Y. Jin, W.J.Cai, Y.W.Wang, et al. A simple dynamic model of cooling coil unit [J]. Energy conversation and Management,2006,47:2659-2672.
[16]L.Lu, W.J.Cai, Y. C. Soh, et al. HVAC system optimization--condenser water loop [J]. Energy conversation and Management,2004,45:613-630.
[17]W.Z.Huang, M. Zaheeruddin, S.H. Cho. Dynamic simulation of energy management control functions for HVAC systems in buildings [J]. Energy conversation and Management,2006, 47:926-943.
[18]B.Tashtoush, M. Molhim, M. Al-Rousan. Dynamic model of an HVAC system for control analysis [J]. Energy,2005,30:1729-1745.
[19]Z.J.Ma, S.W.Wang, X.H.Xu, et al. A supervisory control strategy for building cooling water systems for practical and real time applications [J]. Energy Conversion and Management 2008; 49(8):2324-36.
[20]Z.J.Ma, S.W.Wang. An optimal control strategy for complex building central chilled water systems for practical and real-time applications [J]. Building and Environment,2009,44: 1188-1198.
[21]G. P. Henze, B.Biffar, D.Kohn, et al. Optimal design and operation of a thermal storage system for a chilled water plant serving pharmaceutical buildings [J]. Energy and Buildings,2008,40:1004-1019.
[22]C.Furlong. Optimizing Chillers and Towers [J]. ASHRAE,2004,46(7):123-129.
[23]F.W.Yu, K.T.Chan. Optimization of water-cooled chiller system with load-based speed control [J]. Energy Conversion and Management, Applied Energy,2008,85:931-950
[24]Y.J.Sun, S. W.Wang, G.S.Huang. Chiller sequencing control with enhanced robustness for energy efficient operation [J]. Energy and Buildings,2009,41:1246-1255.
[25]S.W.Wang, X.Q.Jin.Model-based optimal control of VAV air-conditioning system using genetic algorithm [J]. Buiding and Environment,2000,35(6):471-487.
[26]N.Nassif, S.Kajl, R.Sabourin. Simplified model-based optimal control of VAV air conditioning system [J]. Building Simulation,2005:823-830.
[27]赵相彬,赵哲身,孙怡佳.VAV空调系统的变静压控制和研究方法[J].微计算机信息,2005,21(6)：17-19.
[28]N.Nassif, S.Moujaes. A cost-effective operating strategy to reduce energy consumption in a HVAC system [J]. International Journal of Energy Research,2008,32:543-558.
[29]T.Y.Chen. Real-time predictive supervisory operation of building thermal systems with thermal mass [J]. Energy and Buildings,2001,33:141-150.
[30]L.Lu, W.J.Cai, Y.C.Soh, et al. Global optimization for overall HVAC systems--Part Ⅰ problem formulation and analysis [J]. Energy Conversion and Management,2005,46: 999-1014.
[31]L.Lu, W.J.Cai, Y.C.Soh, et al.Global optimization for overall HVAC systems-Part Ⅱ problem solution and simulations [J]. Energy Conversion and Management,2005,46: 1015-1028.
[32]G.R.ZHENG, M.Zaheer-uddin.Optimization of thermal processes in a variabal air volume HVAC system [J]. Energy,1996,21(5):407-420.
[33]W.Z.Huang, M.Zaheer-uddin, S.H.Cho. Dynamic simulation of energy management control functions for HVAC systems in buildings [J]. Energy Conversion and Management,2006, 47:926-943.
[34]A.Kusiak, M.Y. Li, F.Tang. Modeling and optimization of HVAC energy consumption [J], Applied Energy,2010,87:3092-3102.
[35]GJahedi, M.M.Ardehali. Wavelet based artificial neural network applied for energy efficiency enhancement of decoupled HVAC system [J]. Energy Conversion and Management,2012,54:47-56.
[36]M.A.Piette, S.Kiliccote.Characterization and Demonstration of Demand Responsive Control Technologies and Strategies in Commercial Buildings[R].California:LBNL, Tech Rep, 2006:1-23.
[37]L.K.Rawlings. Ice storage system optimization and control strategies [J]. ASHRAE Transactions,1985,91 (2):12-23.
[38]J.E.Braun. A comparison of chiller priority, storage priority, and optimal control of an ice storage system [J]. ASHRAE Transactions,1992,98(1):893-902.
[39]何大四,张旭.蓄冷系统新型节能优化运行控制方法[J].建筑科学,2007,23(2)：27-30.
[40]李先庭,李寒春,石文星.基于设计周期负荷的冰蓄冷系统设计[J].暖通空调,2007,37(5)：83-88.
[41]J.E.Braun.Load control using building thermal mass [J].Journal of solar energy engineering-transactions of the ASME,2003,125(3):292-301.
[42]P. Xu, P. Haves. Case study of demand shifting with thermal mass in two large commercial buildings [J]. ASHRAE Transactions 2006,2006,112(1):572-580.
[43]J. Roth, J. Dieckmann, J. Brodrick. Using Off-Peak Precooling [J]. ASHRAE JOURNAL, 2009,51(3):80-83.
[44]S.Morgan, M.Krarti. Impact of electricity rate structures on energy cost savings of pre-cooling controls for office buildings [J]. Building and Environment,2007,42(8): 2810-2818.
[45]K.Srinivas, N.Lu. Evaluation of Residential HVAC Control Strategies for Demand Response Programs [J]. ASHRAE Trans,2006,112(2):535-546.
[46]J.R.Ma, J.Qin, T.Salsbury, et al. Demand reduction in building energy systems based on economic model predictive control [J]. Chemical Engineering Science,2012 67:92-100.
[47]蔡文剑,贾磊,王雷,杜晓通.建筑节能技术与工程基础[M].北京：机械工业出版社,2007.
[48]K.Coughlin, M.A.Piette, C.Goldman, et al. Estimating demand response load impact: evaluation of baseline load models for non-residential buildings in [R], California: LBNL, Tech Rep,2008,1-28.
[49]K.Coughlin, M.A.Piette, C.Goldman, et al. Statistical analysis of baseline load models for non-residential buildings [J]. Building and Environment,2009,41:374-381.
[50]S.J.Inkinen, Y. Neuvo. Base line estimation using sparse nonlinear operators [J]. Signal Processing,2002,82:811-817.
[51]S.Bukvic, D.Spasojevic. An alternative approach to spectrum base line estimation [J]. Spectrochimica Acta Part B,2005,60:1308-1315.
[52]D. Reza, A. Saeed. Demand response regulation modeling based on distribution system asset efficiency [J]. Electric Power Systems Research,2011,81:667-676.
[53]J.H.Wang, N.B.Cary, Z.G.Hu, et al. Demand response in China [J]. Energy,2010, 35:1592-1597.
[54]O.Sezgena, C.A.Goldmana, P.Krishnaraob. Option value of electricity demand response [J]. Energy,2007,32:108-119.
[55]P.Cappers, C.Goldman, D.Kathan. Demand response in U.S. electricity markets:Empirical evidence [J]. Energy,2010,35:1526-1535.
[56]M. H Albadi, E.F.Eisaadany. A summary of demand response in electricity markets [J]. Electric Power Systems Research,2008,78:1989-1996.
[57]J.H.KIM, A.Shcherbakova. Common failures of demand response [J]. Energy,2011,36: 873-880.
[58]C.I.Hsu, Y.H.Wen. Reliability evaluation for airline network design in response to fluctuation in passenger demand [J]. Omega,2002,30:197-213.
[59]R. AAZAMI, K.AFLAKI, M.R.HAGHIFAM. A demand response based solution for LMP management in power markets [J]. Electrical Power and Energy Systems,2011,11:1-8.
[60]崔航,王冕,罗贵明,等.基于扩展时序距离法和自适应控制的超短期负荷预测[J].电力系统自动化,2009,33(15)：38-42.
[61]黎灿兵,李晓辉,赵瑞,等.电力短期负荷预测相似日选取算法[J].电力系统自动化,2008,32(9)：69-73.
[62]畅广辉,刘涤尘,熊浩.基于多分辨率SVM回归估计的短期负荷预测[J].电力系统自动化,2007,31(9)：37-41.
[63]何大四,张旭.改进的季节性指数平滑法预测空调负荷分析[J].同济大学学报：自然科学版,2005,33(12)：1672-1676.
[64]万迪光.不确定输入对电力负荷预测的影响[J].山东大学学报：工学版,2004,34(5)：55-58.
[65]高荣,刘晓华.基于小波变换的支持向量机短期负荷预测[J].山东大学学报：工学版,2005,35(3)：115-118.
[66]D.M.Tsai, C.C.Lin. Fuzzy C-means based clustering for linearly and nonlinearly separable data Pattern Recognition [J].2011,44:1750-1760
[67]徐巍,陈祥光,彭红星,等.基于模糊C-均值聚类与支持向量机的PMV指标预测系统[J].系统工程理论与实践,2009,29(7)：119-124.
[68]王旭平,王汉功,陈小虎.基于模糊C均值算法的齿轮泵故障诊断技术[J].机床与液压,2009,37(10)：269-272.
[69]J.W.Liu, M.Z.Xu.Kernelized fuzzy attribute C-means clustering algorithm Fuzzy Sets and Systems [J] Fuzzy Sets and Systems,2008,159:2428-2445.
[70]J.C.Bezdek, R.Ehrlich, W.Full. FCM:The fuzzy C-means clustering algorithm [J] Computers & Geosciences,1984,10:191-203.
[71]胡守仁.神经网络导论[M].北京：国防科技大学出版社,1993.
[72]王伟.人工神经网络原理——入门与应用[M].北京：北京航空航天大学出版社,1995.
[73]刘伟.采用负荷预测的冰蓄冷空调系统[D].西安：西安建筑科技大学,2005.
[74]A.P.Plumb, C.R.Rowe, P.York, et al. Optimisation of the predictive ability of artificial neural network (ANN) models:A comparison of three ANN programs and four classes of training algorithm [J]. European Journal of Pharmaceutical Sciences,2005,25:395-405.
|75] R.Battiti. First- and second-order methods for learning:between steepest descent and Newton's method [J].Neural Comput,1992,4:141-166.
[76]C.Q. Li, S.L. Guo, J. Zhang. Modified NLPM-ANN model and its application [J]. Journal of Hydrology,2009,378:137-141.
[77]B.Pang, S.L.Guo, L.H Xiong, et al.2007. A nonlinear perturbation model based on artificial neural network [J]. Journal of Hydrology,2007,333:504-516.
[78]马庆,李歧强,聂清珍.公共建筑空调温度设定值的动态优化控制研究[J].系统工程学报,2011,26(4)：435-441.
[79]C.A.Balaras. The role of thermal mass on the cooling load of buildings. An overview of computational methods [J]. Energy and Buildings,1996,24:1-10.
[80]E.H.Mathews, P.G.Rousseau, P.G.Richards, et al. A procedure to estimate the effective heat storage capability of a building [J].Building and Environment,1991,26(2):179-188.
[81]L.Zhu, R.Hurt, D.Correia, et al. Detailed energy saving performance analyses on thermal mass walls demonstrated in a zero energy house [J]. Energy and Buildings,2009,41: 303-310.
[82]K.J.Kontoleon, D.K. Bikas.The effect of south wall's outdoor absorption coefficient on time lag, decrement factor and temperature variations [J]. Energy and Buildings,2007,39 (9):1011-1018.
[83]K.Gregory, B.Moghtaderi, H.Sugo, et al. Effects of thermalmass on the thermal performance of various Australian residential constructions systems [J]. Energy and Buildings,2008,40 (4):459-465.
[84]P.Burberry.Practical Thermal Design in Buildings [M], London:Batsford Academic and Educational Ltd.,1983.
[85]B. Givoni. Man. Climate and Architecture,2nd ed. [M]. London:Applied Science,1981.
[86]J.L.Zhou, G.Q.Zhang, Y.L.Lin, et al. Energy analysis of buildings employing thermal mass in Cyprus [J].Energy and Buildings,2008,40 (6):979-986.
[87]J.N. Hacker, T.P. De Saulles, A.J. Minson, et al. Embodied and operational carbon dioxide emissions from housing:a case study on the effects of thermal mass and climate change [J], Energy and Buildings,2008,40 (3):375-384.
[88]E. Shaviv, A. Yezioro, I. Capeluto. Thermal mass and night ventilation as passive cooling design strategy [J].Renewable Energy,2001,24 (3/4):445-452.
[89]M. Brown. Optimization of thermal mass in commercial building applications, Journal of Solar Energy Engineering [J]. Transactions of the ASME,1990,112 (4):273-279.
[90]T. Kusuda, J. Bean. Comparison of calculated hourly cooling load and indoor temperature with data for high mass building tested in an environmental chamber [J]. ASHRAE Transactions,1981,87:1232-1240.
[91]D. Burch, S. Malcolm, K. Davis. The effect of wall mass on the summer space cooling of six test buildings [J]. ASHRAE Transactions,1984,90:5-21.
[92]J.E.Braun. Reducing energy costs and peak electrical demand through optimal control of building thermal storage [J].ASHRAE Transactions,1990,96(2):876-888.
[93]L.N.Yang, Y.G.Li. cooling load reduction by using thermal mass and night ventilation [JJ.Energy and Buildings,2008,40:2052-2058.
[94]M.Kawashima, C.E.Dorgan, J.W.Mitchell. Hourly Thermal Load Prediction for the Next 24 Hours by ARIMA, EWMA, LR, and an Artificial Neural Network [J]. ASHRAE Transactions,1995,101(1):186-200
[95]J.W.MacArthur. On-line recursive estimation for load profile prediction [J].ASHRAE Transactions,1989,95(1):621-628.
[96]王勇.冰蓄冷系统优化控制技术的研究[D].清华大学,1997.
[97]M.Kawashima, C.E.Dorgan, J.W.Mitchell. Hourly Thermal Load Prediction for the Next 24 Hours by ARIMA, EWMA, LR, and an Artificial Neural Network [J]. ASHRAE Transactions,1995,101(1):186-200
[98]T.Y.Chen, A.K.Athienitis. Ambient temperature and solar radiation prediction for predictive control of HVAC systems and a methodology for optimal building heating dynamic operation [J]. ASHRAE Transactions,1996,102(1):26-36.
[99]F.C.麦奎斯顿,J.D.帕克,J.D.斯皮特勒.空调工程[M].俞炳丰.北京：化学工业出版社,2005.
[100]申欢迎,秦萍,董军.环境因素对PMV指标的影响分析[J].制冷与空调,2003,(4),27-29.
[101]叶海,魏润柏.热环境客观评价的一种简易方法[J].人类工效学,2004,10(3)：16-19.
[102]K.H.Lee, J.E.Braun. Evaluation of methods for determining demand-limiting set point trajectories in buildings using short-term measurements [J]. Building and Environment, 2008,43(10):1769-1783.
[103]K.H.Lee, J.E.Braun. Development of methods for determining demand-limiting set point trajectories in buildings using short-term measurements [J]. Building and Environment, 2008,43(10):1755-1768.
[104]K.H.Lee, J.E.Braun. Model-based demand-limiting control of building thermal mass [J]. Building and Environment,2008,43(10):1633-1646.
[105]A. Rabl, L.K. Norford. Peak load reduction by preconditioning buildings at night [J], International Journal of Energy Research,1991,15:781-798.
[106]N.Zhu, S.W.Wang, Z.J.Ma, et al. Energy performance and optimal control of air-conditioned buildings with envelopes enhanced by phase change materials [J], Energy Conversion and Management,2011,52:3197-3205.
[107]K.R. Keeney, J.E. Braun. Application of building precooling to reduce peak cooling requirements [J], ASHRAE Transactions,1997,103:463-469.
[108]P. Xu, P. Haves, J.E. Braun, et al.Peak demand reduction from pre-cooling with zone temperature reset in an office building [A], Proceedings of 2004 ACEEE Summer Study of Energy Efficiency in Buildings [C], Pacific Grove, CA,2004:1235-1239.
[109]R.X.Yin, P.Xu, M. A.Piette, et al. Study on Auto-DR and pre-cooling of commercial buildings with thermal mass [J]. Energy and Buildings,2010,42:967-975.
[110]胡钦华,李奎山,Albert T P So居民建筑夏季提高室内空调温度设定值的可行性研究[J].东莞理工学院学报,2008,15(3)：104-107.
[111]杜雪伟,王海斌,杜志敏,等.基于提前启动和建筑储能的VAV系统优化运行策略[J]. 建筑热能通风空调,2007,26(5)：7-10.
[112]李建兴,涂光备,涂岱昕.多泵并联水系统的技术经济分析[J].流体机械,2004,32(10)：38-41.
[113]李彬,肖勇全,李德英,等.变流量空调水系统的节能探讨[J].暖通空调,2006,36(1)：132-136.
[114]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建[J].建筑科学,2007,23(6)：12-15.
[115]李苏泷,邹娜.空调冷却水变流量控制方法研究[J].暖通空调,2005,35(12)：51-53.
[116]刘亮,孙育英,杨超.二次泵变流量水系统的设计[J].暖通空调,2007,37(9)：110-115.
[117]罗新梅,周向阳,曾祖铭.水泵变流量运行性能分析[J].华东交通大学学报,2004,21(4)：19-21.
[118]孟彬彬,朱颖心,林波荣.部分负荷下一次泵水系统变流量性能研究[J].2002,32(6)：108-110.
[119]符永正.水泵并联运行的流量增量及相关问题分析[J].暖通空调,2004,34(11)：83-85.
[120]董宝春,刘传聚,杨伟.变流量水系统优化运行的探讨[J].暖通空调,2007,37(1)：55-59.
[121]董宝春,刘传聚,刘东,等.一次泵/二次泵变流量系统能耗分析[J].暖通空调,2005,35(7)：82-85.
[122]T.S.Lee, K.Y.Liao, W.C.Lu. Evaluation of the suitability of empirically-based models for predicting energy performance of centrifugal water chillers with variable chilled water flow [J]. Applied Energy,2012,93:583-595.
[123]S.W.Wang, Z.J.Ma, D.C.Gao. Performance enhancement of a complex chilled water system using a check valve:Experimental validation [J]. Applied Thermal Engineering, 2010,30:2827-2832.
[124]Z.J.Ma, S.W.Wang. An optimal control strategy for complex building central chilled water systems for practical and real-time applications [J]. Building and Environment,2009, 44:1188-1198.
[125]F.W. Yu, K.T. Chan. Improved energy performance of air cooled centrifugal chillers with variable chilled water flow [J]. Energy Conversion and Management,2008,49:1595-1611.
[126]Hartman T. All-variable speed centrifugal chiller plants [J]. ASHRAE J,2001,43(9):43-53.
[127]I. Dubov. Chilled water plant effciency [J]. ASHRAE J,2003,45(6):37-40.
[128]Saththasivam J, Ng KC. Predictive and diagnostic methods for centrifugal chillers [J]. ASHRAE Trans,2008,114(1):282-7.
[129]T.Moses. Variable-primary flow:important lessons learned [J]. Heating/Piping/Air Condition Eng 2004,76(7):40-43.
[130]M.Liu. Variable water flow pumping for central chilled water systems [J].J Solar Energy Engineering 2002,124(3):300-304.
[131]X.F.Liu, J.P.Liu, J.D.Lu, et al. Research on operating characteristics of direct-return chilled water system controlled by variable temperature difference [J]. Energy,2012,76:1-4.
[132]S.T.Taylor. Primary-only vs. primary secondary variable flow systems [J].ASHRAE J, 2002,44(2):25-9.
[133]J.E.Braun, S.A.Klein, J.W.Mitchell, et al. Applications of optimal control to chilled water systems without storage. ASHRAE Trans 1989,95(1):663-675.
[134]G.Avery. Improving the effciency of chilled water plants [J]. ASHRAE J,2001, 43(5):14-18.
[135]F.W.Yu, K.T.Chan. Optimum load sharing strategy for multiple-chiller systems serving air-conditioned buildings [J]. Build Environment,2007,42(4):1581-1593.
[136]K.A.Brownell, S.A.Klein, D.T. Reindl. Refrigeration system malfunctions [J]. ASHRAE J 1999,41:40-46.
[137]F.W. Yu, K.T.Chan. Tune up the set point of condensing temperature for more energy effcientair cooled chillers [J]. Energy Conversion Manage 2006,47:2499-2514.
[138]F.W.Yu, K.T.Chan. Life cycle analysis of enhanced condenser features for air-cooled chillers serving air-conditioned buildings [J]. Build Environment 2006,41:981-991.
[139]S.W.Wang. Dynamic simulation of a central chilling system and evaluation of EMCS on-line control strategies [J]. Build Environment,1998,33(1):1-20.
[140]M.J.Kempiak, R.R.Crawford. Three-zone steady-state modelling of a mobile air-conditioning condenser [J]. ASHRAE Trans 1992,98(1):475-488.
[141]F.W. Yu, K.T. Chan. Low-energy design for air-cooled chiller plants in air-conditioned buildings [J]. Energy Buildings,2006,38(4):334-339.
[142]T.S.Lee, W.C.Lu. An evaluation of empirically-based models for predicting energy performance of vapor-compression water chillers [J].Applied Energy,2010, 87(11):3486-93.
[143]涂岱昕,李建兴,胡振杰.空调变水量系统水泵变频的相关问题[J].流体机械,2007,35(1)：49-63.
[144]姚国梁.空调变频水泵节能问题探讨[J].暖通空调,2004,34(6)：32-34.
[145]毕崇宁.暖通空调水系统效率优化策略研究[D].山东大学,2008.
[146]杜文学.空调一级泵变流量水系统的管网优化[D].华中科技大学,2005.
[147]杨伟.区域供冷水系统节能运行研究[D].同济大学,2007.
[148]L.Lu, W.J.Cai, L.H.Xie, et al. HVAC system optimization—in-building section [J]. Energy and Buildings,2005(37):11-22.
[149]哈尔滨建筑工程大学.供热工程[M].北京：中国建筑工业出版社,1985.
[150]李建华,康相玖,陶海臣,等.变频控制在中央空调系统的节能可行性分析[J].制冷与空调,2003,3(1)：44-47.
[151]崔景潭,晏刚,周晋,等.变频空调压缩机及变频调速系统的技术现状[J].制冷与空调,2003,3(2)：11-18.
[152]P.L.Luis, O.Jose, R. M.Ismael. Constructing HVAC energy efficiency indicators [J]. Energy and Buildings,2012,47:619-629.
[153]P.L.Luis, O.Jose, R. M.Ismael. The map of energy flow in HVAC systems [J]. Applied Energy,2011,88:5020-5031.
[154]P.L.Luis, O.Jose, C. F. Juan, et al. A review of HVAC systems requirements in building energy regulations [J]. Energy and Buildings,2011,43:255-268.
[155]I.Korolijaa, L.M.Halburdb, Y.Zhang, et al. Influence of building parameters and HVAC systems coupling on building energy performance [J]. Energy and Buildings,2011,43: 1247-1253.
[156]A.P.Wemhoff, M.V. Frank. Predictions of energy savings in HVAC systems by lumped models [J]. Energy and Buildings,2010,42:1807-1814.
[157]N.N.Grace, S.A.Tassou. Dynamic simulation of liquid chillers [A], Proc International Ref rigeration Conference [C]. Purdue,2000:433-470.
[158]M.Ning, M.Zaheeruddin. Neuro-optimal operation of a variable air volume HVAC&R system [J]. Applied Thermal Engineering,2010,30:385-399
[159]葛云亭,彦启森,彭雄兵.制冷空调系统仿真数学模型的理论与实验研究[J].制冷学报,1995,4：9-18.
[160]M.T.Baltaa, I. Dincerb, A.Hepbasli. Performance and sustainability assessment of energy options for building HVAC applications [J]. Energy and Buildings,2010,42:1320-1328.
[161]K.F. Fong, V.I. Hanby, T.T. Chow. HVAC system optimization for energy management by evolutionary programming [J]. Energy and Buildings,2006,38:220-231.
[162]J.F. Montgomerya, S.I.Greena, S.N.Rogak, et al. Predicting the energy use and operation cost of HVAC air filters [J]. Energy and Buildings,2012,47:643-650.
[163]M.Fasiuddin, I. HVAC system strategies for energy conservation in commercial buildings in Saudi Arabia [J]. Energy and Buildings,2011,43: 3457-3466.
[164]E.E.Guillermo, S.H.Isidoro, A.O.Manuel. Application of an energy management and control system to assess the potential of different control strategies in HVAC systems [J]. Energy and Buildings,2010,42:2258-2267.
[165]A.P.Wemhoff. Application of optimization techniques on lumped HVAC models for energy conservation [J]. Energy and Buildings,2010,42:2445-2451.
[166]P.Bryan,Rasmussen,G. Andrew, et al. Control -oriented modeling of Trancritical vapor compression systems [J]. Journal of Dynamic Systems, Measurement, and Control,2004, 126(54):358-361.
[167]S.Soyguder. Intelligent system based on wavelet decomposition and neural network for predicting of fan speed for energy saving in HVAC system [J]. Energy and Buildings,2011, 43:814-822.
[168]B.Rasmussen, A.Alleyne, C.Bullard, et al. Control- oriented modeling and analysis of automotive transcritical AC system dynamics [A].2002 American Control Conference [C]. Anchorage, USA,2002 (4):3111-3116.
[169]A.P.Wemhoff. Calibration of HVAC equipment PID coefficients for energy conservation [J]. Energy and Buildings,2012,45:60-66.
[170]B.P.Rasmussen, A.Alleyne, A.Musser. Model-driven system identification of transcritical vapor compression systems [J]. IEEE Trans on Control Systems Technology,2005, 13(3):444-457.
[171]A.Kusiak, F.Tang, G.L.Xu. Multi-objective optimization of HVAC system with an evolutionary computation algorithm [J]. Energy,2011,36 (5):2440e2449.
[172]S.D.Antonellis, C.M.Joppolo, L.M.Vapor, et al. Simulation and energy efficiency analysis of desiccant wheel systems for drying processes [J]. Energy,2012,37 (1) 336-345.
[173]聂清珍.暖通空调系统节能与节支优化策略研究[D].山东大学,2010.