城市能源规划中建筑冷负荷预测方法研究
|
摘要
城市能源消耗量大幅度增长、常规能源储备量减少、能源利用率普遍偏低是我国面临的主要城市能源问题。而我国城市规划体系中涉及到城市能源的主要是电力规划、热力规划以及燃气规划三个独立的系统,尚没有完善的城市能源规划系统,这就导致了能源规划与城市总体规划脱节、各部门缺乏沟通协调,甚至出现竞争现象等。为此,已有专家提出了基于动态和空间分布的城市能源综合规划方法,该方法考虑了各种能源专项规划的相互协调,从时间和空间分布的角度考虑能源需求的动态特性,以达到能源的合理配置和高效利用。全面、准确的了解建筑冷负荷水平及动态特性是此方法得以实施的关键。城市规划阶段提供的建筑信息有限、缺乏足够有效的建筑能耗统计数据等直接导致常规的冷负荷预测方法难以应用于城市规划阶段。
本文从理论分析、软件模拟、统计预测三个方面探讨了适用于城市规划阶段建筑逐时冷负荷的预测方法。首先从建筑冷负荷影响因素的显著性分析入手,提出了标准建筑模型的限制因子：建筑物理因子、内扰因子和外扰因子；然后以此为基础建立负荷因子法预测模型和DeST软件模拟预测模型。分析了灰色马尔科夫理论的优缺点及在建筑冷负荷预测领域的可行性,提出了模型的建立步骤及应用模式。
本文首先从理论分析的角度建立了负荷因子法预测模型,包括建筑围护结构冷负荷预测模型、新风负荷预测模型、人员负荷预测模型、照明负荷预测模型和设备负荷预测模型五个子模型。并通过不同计算方法的结果比较、模拟与实测结果的比较等角度验证冷负荷指标及动态特性的精度。结果表明：采用不同计算方法计算得到的冷负荷指标的相对误差不超过10%,满足预测精度的要求,且负荷因子法和DeST的模拟结果在动态特性上是一致的；通过北京某办公楼实测数据与预测结果比较,在整个空调期,除了个别时刻外,预测误差基本在20%以内,满足规划阶段对预测精度的要求。结合负荷因子法和灰色马尔科夫理论提出了适用于城市规划阶段建筑冷负荷逐时预测模型的应用模式。然后建立了基于DeST软件的模拟预测模型用于分析城市各类型建筑的冷负荷动态特性。
本文建立的三种建筑逐时冷负荷预测模型可以应用于城市规划的不同阶段：负荷因子法适用于结合具体地块的规划信息,从理论的角度即时分析该地块规划建筑的冷负荷情况；结合灰色马尔科夫预测模型,可以完成下一年该建筑的逐时冷负荷预测；DeST软件模拟可以分析某城市某类型建筑的冷负荷水平。总之,城市规划阶段建筑逐时冷负荷的准确预测,对城市能源系统的优化配置具有重要的指导意义。
Substantial increasing of energy consumption, reducing of conventional energy reserves and low energy efficiency are the main urban energy problems facing China government. However, there is not perfect urban energy planning system. The planning of power supply, heating supply and gas supply are adiminidtrated by different authorities, considered in isolation without coordination, which led to unreasonable utilization of urban energy. Therefore, an integrated urban erengy planning method is provided, which analyzes the dynamical characteristics and spatial distribution of urban energy, considers intercoordination among special energy planning and dynamical balance between energy supply and demand to achieve the goal of reasonable allocation and high efficiency of urban energy. The key to carry out this planning method is the comprehensive and accurate understanding of building cooling load characteristics. However, the conventional prediction methods on building cooling load are difficult to be used at the urban planning stage because of the limited building information and the lack of adequate statistical data on building energy.
In this paper, feasible building cooling load prediction methods are discussed based on theoretical analysis, simulation and statistical analysis. First, the determination of limiting factors for a standard building, including physical factor, internal factor and external factor, is provided after analyzing the significance of building cooling load influence factors. And then the Hourly Cooling Load Factor Method (HCLFM) model and simulation model based on DeST software are developed. At last, the advantages and disadvantages of Grey Markov theory and feasibility applied in building cooling load prediction fieldare analyzed. The steps and application mode are proposed.
The Hourly Cooling Load Factor Method (HCLFM) based on theoretical analysis is developed, which is composed of the building envelope cooling load predicting model, infiltration load predicting model, occupant load predicting model, lighting load predicting model and equipment load predicting model. The predicted results are verified by comparing the results of different calculation methods and analyzing errors between measured results and predicted results. The results show that the prediction relative error of buling cooling load index is less than 10% meeting accurancy requirements in engineering field and the predicted results have the same dynamic characteristics based on HCLFM as that based on DeST software. Take an office building for example; the prediction errors are less than 20% during whole cooling period. The simulation model based on DeST software is developed to predict the dynamic characreristics of building cooling load and Grey-Markov model based on statistical data is developed to predict building cooling load index. Integrating the two methods mentioned above, the application mode of hourly building cooling load prediction model is provided at the urban planning stage.
According to different background conditions, the different methods provided in this paper can be applied to analyze the hourly building cooling load. HCLFM is more feasible to a specific planned plot; the method integrating DeST simulation and Grey-Markov can be used to analyze the building cooling load of a type of building of a city.
In a word, the accurate prediction of the hourly building cooling load has the most importmant significance in allocating and optimizating urban energy system.
本文从理论分析、软件模拟、统计预测三个方面探讨了适用于城市规划阶段建筑逐时冷负荷的预测方法。首先从建筑冷负荷影响因素的显著性分析入手,提出了标准建筑模型的限制因子：建筑物理因子、内扰因子和外扰因子；然后以此为基础建立负荷因子法预测模型和DeST软件模拟预测模型。分析了灰色马尔科夫理论的优缺点及在建筑冷负荷预测领域的可行性,提出了模型的建立步骤及应用模式。
本文首先从理论分析的角度建立了负荷因子法预测模型,包括建筑围护结构冷负荷预测模型、新风负荷预测模型、人员负荷预测模型、照明负荷预测模型和设备负荷预测模型五个子模型。并通过不同计算方法的结果比较、模拟与实测结果的比较等角度验证冷负荷指标及动态特性的精度。结果表明：采用不同计算方法计算得到的冷负荷指标的相对误差不超过10%,满足预测精度的要求,且负荷因子法和DeST的模拟结果在动态特性上是一致的；通过北京某办公楼实测数据与预测结果比较,在整个空调期,除了个别时刻外,预测误差基本在20%以内,满足规划阶段对预测精度的要求。结合负荷因子法和灰色马尔科夫理论提出了适用于城市规划阶段建筑冷负荷逐时预测模型的应用模式。然后建立了基于DeST软件的模拟预测模型用于分析城市各类型建筑的冷负荷动态特性。
本文建立的三种建筑逐时冷负荷预测模型可以应用于城市规划的不同阶段：负荷因子法适用于结合具体地块的规划信息,从理论的角度即时分析该地块规划建筑的冷负荷情况；结合灰色马尔科夫预测模型,可以完成下一年该建筑的逐时冷负荷预测；DeST软件模拟可以分析某城市某类型建筑的冷负荷水平。总之,城市规划阶段建筑逐时冷负荷的准确预测,对城市能源系统的优化配置具有重要的指导意义。
Substantial increasing of energy consumption, reducing of conventional energy reserves and low energy efficiency are the main urban energy problems facing China government. However, there is not perfect urban energy planning system. The planning of power supply, heating supply and gas supply are adiminidtrated by different authorities, considered in isolation without coordination, which led to unreasonable utilization of urban energy. Therefore, an integrated urban erengy planning method is provided, which analyzes the dynamical characteristics and spatial distribution of urban energy, considers intercoordination among special energy planning and dynamical balance between energy supply and demand to achieve the goal of reasonable allocation and high efficiency of urban energy. The key to carry out this planning method is the comprehensive and accurate understanding of building cooling load characteristics. However, the conventional prediction methods on building cooling load are difficult to be used at the urban planning stage because of the limited building information and the lack of adequate statistical data on building energy.
In this paper, feasible building cooling load prediction methods are discussed based on theoretical analysis, simulation and statistical analysis. First, the determination of limiting factors for a standard building, including physical factor, internal factor and external factor, is provided after analyzing the significance of building cooling load influence factors. And then the Hourly Cooling Load Factor Method (HCLFM) model and simulation model based on DeST software are developed. At last, the advantages and disadvantages of Grey Markov theory and feasibility applied in building cooling load prediction fieldare analyzed. The steps and application mode are proposed.
The Hourly Cooling Load Factor Method (HCLFM) based on theoretical analysis is developed, which is composed of the building envelope cooling load predicting model, infiltration load predicting model, occupant load predicting model, lighting load predicting model and equipment load predicting model. The predicted results are verified by comparing the results of different calculation methods and analyzing errors between measured results and predicted results. The results show that the prediction relative error of buling cooling load index is less than 10% meeting accurancy requirements in engineering field and the predicted results have the same dynamic characteristics based on HCLFM as that based on DeST software. Take an office building for example; the prediction errors are less than 20% during whole cooling period. The simulation model based on DeST software is developed to predict the dynamic characreristics of building cooling load and Grey-Markov model based on statistical data is developed to predict building cooling load index. Integrating the two methods mentioned above, the application mode of hourly building cooling load prediction model is provided at the urban planning stage.
According to different background conditions, the different methods provided in this paper can be applied to analyze the hourly building cooling load. HCLFM is more feasible to a specific planned plot; the method integrating DeST simulation and Grey-Markov can be used to analyze the building cooling load of a type of building of a city.
In a word, the accurate prediction of the hourly building cooling load has the most importmant significance in allocating and optimizating urban energy system.
引文
[1]温国平.浅谈建筑能耗与建筑节能[J].山西建筑,2003,29(13)：7-13.
[2]付林,郑中海,江亿,等.基于动态和空间分布的城市能源规划方法[A].2008城市发展与规划国际论坛论文集[C],2008.
[3]龙惟定,白玮,张改景,等.区域建筑能源规划—建筑节能基础[J].建设科技,2008,(06)：60-63.
[4]宋金波,陈利顺.我国能源依赖型城市的能源供应分析[J].能源研究与信息,2004,20(1)：11-17.
[5]International Energy Agency. OECD world energy outlook 2002 [R]. Head of Publications Service, OECD/IEA,2002:32-33.
[6]洪亮平.城市能源战略与城市规划[J].太阳能,2006,1：13-17.
[7]龙惟定.区域节能管理的重要环节—区域建筑能源规划[J],暖通空调,2008,38(3)：31-38.
[8]江泽民.对中国能源问题的思考[J].中国能源,2008,30(4)：5-19.
[9]Kosonen R, Shemeikka J. The use of a simple simulation tool for energy analysis[C]. Building Simulation 97, Fifth international IBPSA conference, Prague Czech Republic, vol.2. September 1997: 369-376.
[10]Ellis M W, Mathews E H. A new simplified thermal design tool for architects [J]. Building and Environment,2001,36:1009-1021.
[11]Forester J R, Wepfer W J. Formulation of a load prediction algorithm for a large commercial building [J]. ASHRAE Trans,1984,90n2B:536-551.
[12]何大四,张旭,刘加平.常用空调负荷预测方法分析比较[J].西安建筑科技大学学报(自然科学版),2006,38(1)：125-129.
[13]李爱旗.重庆市小城镇居住建筑热环境分析和建筑冷、热负荷预测研究[D].重庆：重庆大学,2006.
[14]Macarthur J W, Mathur A, Zhao J. On-line recursive estimation for load profile prediction [J]. ASHRAE Trans 1989,95 nl:621-628.
[15]孙靖,程大章.基于季节性时间序列模型的空调负荷预测[J].电工技术学报,2004,19(3)：88-93.
[16]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.
[17]陈柳.小波时间序列在空调负荷预测中的应用[J].流体机械,2008,36(2)：83-86.
[18]王四清,王先民,李元旦,等.系统负荷的灰色区间预测[J].系统工程,1999,17(3)：60-65.
[19]Robinson D, Campbell N, Gaiser W, et al. SUNtool—A new modeling paradigm for simulating and optimizing urban sustainability [J]. Solar Energy,2007,81:1196-1211.
[20]Ferrano F J, Wong K V. Prediction of thermal storage loads using a neural network [J]. ASHRAE Trans,1990,96 n2:723-726.
[21]Kreider J F, Wang X A. Artificial neural networks demonstration for automated generation of energy use predictors for commercial buildings [J]. ASHRAE Trans,1991,97(2):775-779.
[22]Kreider J F, Harberl J S. prediction hourly building energy usage [J]. ASHRAE Journal,1994,36(6): 72-81.
[23]Kreider J F, Harberl J S. prediction hourly building energy usage:the great energy predictor shootout-overview and discussion of results [J]. ASHRAE Trans,1994,100(2):1104-1118.
[24]Mattias B.O.Ohlsson. Predicting system loads with artificial neural network methods and results from "the great energy predictor shootout" [J]. ASHRAE Trans,1994,100(2):1063-1074.
[25]Kashiwaqi, Norihito. Heating and cooling load prediction using a neural network system [C]. Proceedings of the International Joint Conference on Neural Networks, vl,1993:939-942.
[26]Hou Z J, Lian Z W, Yao Y, et al. Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique [J]. Applied Energy,2006(83):1033-1046.
[27]Abdullatif E. Ben-Nakhi, Mohamed A. et al. Cooling load prediction for buildings using general [J]. Energy Conversion and Management,2004(45):2127-2141.
[28]郑慧凡,白静,李安桂.BP神经网络在建筑物空调负荷预测中的应用评述[J].计算机应用与软件,2007,24(6)：103-105.
[29]程国建,张庆红.Elman网络与BP网络在空调负荷预测中的对比研究[J].网络与应用,2007：23-25.
[30]吴杰,陈光明.空调冷负荷的人工神经网络预测分析[A].中国工程热物理学会第十届年会论文集[C],2001：611-616.
[31]曹双华,曹家枞.基于神经网络的空调负荷混沌优化预测[J].东华大学学报,2003,29(1)：53-56.
[32]曹双华,曹家枞.基于小波变换的神经网络空调负荷预测研究[J].暖通空调,2005,35(4)：13-17.
[33]姚哗,连之伟,侯志坚,等.基于AHP的空调负荷组合预测研究[J].哈尔滨工业大学学报,2004,36(9)：1269-1271.
[34]Yao Y, Lian Z W, Liu S Q, et al. Hourly cooling load prediction by a combined forecasting model based on Analytic Hierarchy Process [J]. International Journal of Thermal Sciences,2004,43: 1107-1118.
[35]董兴杰,周晓玲,朱赛霞.冰蓄冷空调负荷模糊预测原理及软件[J].制冷空调,2006,27(8)：22-25.
[36]陈柳,吴冬梅.空调负荷预测探讨[J].制冷,2007,26(3)：32-34.
[37]李琼,孟庆林,吉野博,等.基于支持向量机的建筑物空调负荷预测模型[J].暖通空调,2008,38(1)：14-18.
[38]Li Q, Meng Q L, Cai J J, et al. Applying support vector machine to predict hourly cooling load in the building [J]. Applied Energy,2009,86:2249-2256.
[39]Li Q, Meng Q L, Cai J J, et al. Predicting hourly cooling load in the building:A comparison of support vector machine and different artificial neural networks [J]. Energy Conversion and Management,2009, 50:90-96.
[40]Chow T T, Fong K F, Chan A L S, et al. Energy modeling of district cooling system for new urban development [J]. Energy and Buildings,2004,36:1153-1162.
[41]Yik F W H, Burnett J, Prescott I. Predicting air-conditioning energy consumption of a group of buildings using different heat rejection methods [J]. Energy and Buildings,2001,33:151-166.
[42]瞿燕.上海世博园区空调动态负荷预测与研究[J].暖通空调,2008,38(10)：9-17.
[43]Kosonen R, Shemeikka J. The use of a simple simulation tool for energy analysis[C]. Building Simulation 97, Fifth international IBPSA conference, Prague Czech Republic, vol.2. September 1997: 369-376.
[44]戴慎志.城市工程系统规划[M].北京：中国建筑工业出版社,1999.
[45]王耀春,陈汝冬.一种适合于冰蓄冷空调系统的全年逐时负荷计算方法[J].流体机械,2004,32(12)：69-71.
[46]苑翔,龙惟定.应用情景分析法预测区域建筑冷负荷[A].全国暖通空调制冷2008年学术年会资料集[C],2008.
[47]翟燕,潘毅群,黄治钟.上海世博园区空调负荷变化情景分析[J].建筑节能,2008,36(7)：12-17.
[48]中国城市规划设计研究院.GBJ 137-90城市用地分类与规划建设用地标准[S].北京：中国计划出版社,1991.
[49]钟新基,杨园英,何宜庚.土地与土地利用[M].北京：地震出版社,1994.
[50]厉美飞.典型办公室建筑空调设计负荷特性的模拟分析[D].重庆：重庆大学,2006.
[51]何天祺,牙侯专,张萍.办公建筑空调设计冷负荷相关因素的考察与分析[J].重庆建筑大学学报,1998,20(6)：56-60.
[52]董智慧,徐书朋,刘凡.建筑形状比对办公建筑冷(热)负荷的影响分析[J].节能,2008,(3)：35-37.
[53]董智慧,刘凡,庞俊香.建筑窗墙比对办公建筑冷(热)负荷的影响分析[J].建筑节能,2008,36(3)：6-9.
[54]Depecker P, Menezo C, Virgone J, et al. Design of buildings shape and energetic consumption [J]. Building and Environment,2001, (36):627-635.
[55]Ramzi O, Adnan A, Moncef K. A simplified analysis method to predict the impact of shape on annual energy use for office building [J]. Energy Conversion and Management,2007, (48):300-305.
[56]Adnan A, Donghyun S, Moncef K. Impact of building on thermal performance of office building in Kuwait [J]. Energy Conversion and Management,2009,50:822-828.
[57]姜益强,张志强,姚杨,等.用EnergyPlus模拟检验影响节能办公建筑的因素[J].建筑科学,2006,22(6)：22-26.
[58]苑翔,龙惟定.区域建筑冷负荷预测中的朝向因素分析与转换[J].土木建筑与环境工程,2009,31(6)：96-101.
[59]滕素珍,冯敬海.数理统计学[M].大连：大连理工大学出版社,2005.
[60]中国有色工程设计研究总院.GB 50019—2003采暖通风与空气调节设计规范[S].北京：中国计划出版社,2004.
[61]中国建筑科学研究院.GB 50189—2005公共建筑节能设计标准[S].北京：中国建筑工业出版社,2004.
[62]赵坚,刘金祥.基于BIN法的空调建筑全年能耗影响因素分析[J].节能技术,2007,25(4)：317-320.
[63]田胜元,萧日嵘.试验数据处理与分析[M].北京：中国建筑工业出版社,1988.
[64]何少华,文竹青,娄涛.试验设计与数据处理[M].湖南：国防科技大学出版社,2002.
[65]王立雄.建筑节能[M].北京：中国建筑工业出版社,2009.
[66]李良,杨文斌,韩春凤.民用建筑最佳节能体型的研究[J].工业建筑,2000,30(8)：21-22.
[67]曹毅然,陆善后,范宏武,等.建筑物体形系数与节能关系的探讨[J].住宅科技,2005,(4)：26-28.
[68]邹洪波.建筑节能体形的优化设计研究[J].建筑节能,2007,35(11)：26-27.
[69]王朋.建筑空调负荷动态计算分析[D].上海：上海交通大学,2007.
[70]Rees S.J, Spitler J.D, Haves P. Quantitative Comparison of North American and U.K.Cooling Load Calculation Procedures—Results [J]. ASHRAE Transactions,1998,104(2):47-61.
[71]谷波.建筑物空调负荷计算分析[M].北京：科学出版社,2003.
[72]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,2002.
[73]邓聚龙.灰理论基础[M].武汉：华中科技大学出版社,2002.
[74]王明明,赵宝元,刘小峰.运筹与决策基础[M].北京：中国林业出版社,2001.
[75]朱孔来.灰色马尔科夫链预测模型及其应用[J].系统工程理论与实践,1999,2：33-37.
[76]王露璐,邓丽,刘先涛.股票投资价值灰色马尔可夫预测[J].商业研究,2004,285(01)：137-139.
[77]陈赣洪.灰色马尔柯夫模型在保险预测中的应用[J].统计与预测,1994,13(02)：35-37.
[78]刘伟洁,詹棠森.灰色马尔可夫链在物流总额预测中的研究应用[J].办公自动化,2009,166(10)：25-26.
[79]叶学军,杨应玖.二次参数拟合灰色马尔科夫链商品房价格预测模型[J].水利电力将科技,1994,21(04)：72-76.
[80]安春晖,王建文.火灾事故的灰色马尔柯夫模型预测[J].消防科学与技术,2008,27(05)：367-370.
[81]张启明.灰色马尔可夫预测模型及在地震预报中的应用[J].地震科学,1994,25(01)：25-29.
[82]钱卫东,刘志强.基于灰色马尔可夫的道理交通事故预测[J].中国安全科学学报,2008,18(03)：33-36.
[83]陈水校.粘虫发生量的灰色马尔柯夫链预测[J].科技通报,1991,7(03)：150-152.
[84]戴淼,向亚丽.灰色马尔可夫模型的建设用地预测[J].国土资源科技管理,2007,24(04)：101-103.
[85]冯江浪.改进灰色马尔科夫模型及其在水资源预测中的应用[J].物探化探计算技术,2010,32(01)：109-112.
[86]石扬.基于灰色马尔可夫模型的能耗预测[J].山东轻工业学院学报,2009,23(02)：63-65.
[87]耿波,王君杰,张谢东.桥梁技术状况预测的灰色马尔科夫链模型研究[J].武汉理工大学学报,2007,31(01)：107-110.
[88]Scherer W T, Glagola D M. An overview of Markovian models fot bridge management system[C]. Proceedings of the ASCE conference on Infrastructure Planning and Management,1993:382-386.
[89]Kleiner Y. Scheduling inspection and renewal of large infrastructure assets[J]. Infrastructure System, 20017(04):136-143.
[90]Abraham D M, Wirahadikusuamh R, Short T J, et al. Optimization modeling for sewer network management [J]. Construction Engineering and Management,1998,124(05):402-410.
[91]李艳昌.基于灰色马尔科夫的残差蚁群灰色模型在负荷预测中的应用[J].华东电力,2007,35(07)：602-605.
[92]李芳.灰色理论在负荷预测中的应用[J].硅谷,2008,7(01)：12-13.
[93]吉培荣,邹红波,张玉文.无偏灰色预测模型在电力系统负荷预测中的应用[J].三峡大学学报(自然科学版),2005,27(04)：318-320.
[94]王白玲.电力负荷组合预测的理论方法及影响因素分析[D].北京：华北电力大学,2005.
[95]李恒.基于马尔可夫理论埋地燃气钢管腐蚀预测研究[D].大连：大连理工大学,2007.
[96]邓聚龙.灰色系统(社会经济)[M].北京：国防工业出版社,1983.
[97]李骥,邹瑜,谓峥.建筑能耗模拟软件的特点及应用中存在的问题[J].建筑科学,2010,26(02)：24-28.
[98]苏华.建筑物动态能耗分析用气象仿真模型研究[D].重庆：重庆大学,2002.
[99]吕政.建筑围护能耗的计算机仿真设计介绍[J].制冷空调与电力机械,2006,27(6)：30-34.
[100]Jiang Y. State-space method for analysis of the thermal behavior of room and calculation of air conditioning load[J]. ASHRAE Trans,1982,88:122-132.
[101]刘迎云.户式中央空调系统的经济性研究[D].湖南：湖南大学,2001.
[102]黑龙江省建筑设计院.JBJ 39—87托儿所、幼儿园建筑设计规范[S].北京：中国建筑工业出版社,1988.
[103]张宗尧,赵秀兰.托幼中小学校建筑设计手册[M].北京：中国建筑工业出版社,1999.
[104]天津市建筑设计院.GBJ 99—86中小学校建筑设计规范[S].北京：中国计划出版社,1987.
[105]中华人民共和国教育部.城市普通中小学校校舍建设标准[S].北京：高等教育出版社,2002.
[106]中华人民共和国国家教育委员会.普通高等学校建筑规划面积指标[M].北京：高等教育出版社,1992.
[107]西安建筑科技大学.JBJ 76—2003特殊教育学院建筑设计规范[S].北京：中国建筑工业出版社,2004.
[108]中国勘察设计协会技术经济委员会.民用建筑规划设计定额指标[M].北京：中国计划出版社,1995.
[109]上海市民用建筑设计院.JBJ 49—88综合医院建筑设计规范[S].北京：中国建筑工业出版社,1989.
[110]中华人民共和国卫生部.综合医院建设标准建标110-2008[M].北京：中国计划出版社,2008.
[2]付林,郑中海,江亿,等.基于动态和空间分布的城市能源规划方法[A].2008城市发展与规划国际论坛论文集[C],2008.
[3]龙惟定,白玮,张改景,等.区域建筑能源规划—建筑节能基础[J].建设科技,2008,(06)：60-63.
[4]宋金波,陈利顺.我国能源依赖型城市的能源供应分析[J].能源研究与信息,2004,20(1)：11-17.
[5]International Energy Agency. OECD world energy outlook 2002 [R]. Head of Publications Service, OECD/IEA,2002:32-33.
[6]洪亮平.城市能源战略与城市规划[J].太阳能,2006,1：13-17.
[7]龙惟定.区域节能管理的重要环节—区域建筑能源规划[J],暖通空调,2008,38(3)：31-38.
[8]江泽民.对中国能源问题的思考[J].中国能源,2008,30(4)：5-19.
[9]Kosonen R, Shemeikka J. The use of a simple simulation tool for energy analysis[C]. Building Simulation 97, Fifth international IBPSA conference, Prague Czech Republic, vol.2. September 1997: 369-376.
[10]Ellis M W, Mathews E H. A new simplified thermal design tool for architects [J]. Building and Environment,2001,36:1009-1021.
[11]Forester J R, Wepfer W J. Formulation of a load prediction algorithm for a large commercial building [J]. ASHRAE Trans,1984,90n2B:536-551.
[12]何大四,张旭,刘加平.常用空调负荷预测方法分析比较[J].西安建筑科技大学学报(自然科学版),2006,38(1)：125-129.
[13]李爱旗.重庆市小城镇居住建筑热环境分析和建筑冷、热负荷预测研究[D].重庆：重庆大学,2006.
[14]Macarthur J W, Mathur A, Zhao J. On-line recursive estimation for load profile prediction [J]. ASHRAE Trans 1989,95 nl:621-628.
[15]孙靖,程大章.基于季节性时间序列模型的空调负荷预测[J].电工技术学报,2004,19(3)：88-93.
[16]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.
[17]陈柳.小波时间序列在空调负荷预测中的应用[J].流体机械,2008,36(2)：83-86.
[18]王四清,王先民,李元旦,等.系统负荷的灰色区间预测[J].系统工程,1999,17(3)：60-65.
[19]Robinson D, Campbell N, Gaiser W, et al. SUNtool—A new modeling paradigm for simulating and optimizing urban sustainability [J]. Solar Energy,2007,81:1196-1211.
[20]Ferrano F J, Wong K V. Prediction of thermal storage loads using a neural network [J]. ASHRAE Trans,1990,96 n2:723-726.
[21]Kreider J F, Wang X A. Artificial neural networks demonstration for automated generation of energy use predictors for commercial buildings [J]. ASHRAE Trans,1991,97(2):775-779.
[22]Kreider J F, Harberl J S. prediction hourly building energy usage [J]. ASHRAE Journal,1994,36(6): 72-81.
[23]Kreider J F, Harberl J S. prediction hourly building energy usage:the great energy predictor shootout-overview and discussion of results [J]. ASHRAE Trans,1994,100(2):1104-1118.
[24]Mattias B.O.Ohlsson. Predicting system loads with artificial neural network methods and results from "the great energy predictor shootout" [J]. ASHRAE Trans,1994,100(2):1063-1074.
[25]Kashiwaqi, Norihito. Heating and cooling load prediction using a neural network system [C]. Proceedings of the International Joint Conference on Neural Networks, vl,1993:939-942.
[26]Hou Z J, Lian Z W, Yao Y, et al. Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique [J]. Applied Energy,2006(83):1033-1046.
[27]Abdullatif E. Ben-Nakhi, Mohamed A. et al. Cooling load prediction for buildings using general [J]. Energy Conversion and Management,2004(45):2127-2141.
[28]郑慧凡,白静,李安桂.BP神经网络在建筑物空调负荷预测中的应用评述[J].计算机应用与软件,2007,24(6)：103-105.
[29]程国建,张庆红.Elman网络与BP网络在空调负荷预测中的对比研究[J].网络与应用,2007：23-25.
[30]吴杰,陈光明.空调冷负荷的人工神经网络预测分析[A].中国工程热物理学会第十届年会论文集[C],2001：611-616.
[31]曹双华,曹家枞.基于神经网络的空调负荷混沌优化预测[J].东华大学学报,2003,29(1)：53-56.
[32]曹双华,曹家枞.基于小波变换的神经网络空调负荷预测研究[J].暖通空调,2005,35(4)：13-17.
[33]姚哗,连之伟,侯志坚,等.基于AHP的空调负荷组合预测研究[J].哈尔滨工业大学学报,2004,36(9)：1269-1271.
[34]Yao Y, Lian Z W, Liu S Q, et al. Hourly cooling load prediction by a combined forecasting model based on Analytic Hierarchy Process [J]. International Journal of Thermal Sciences,2004,43: 1107-1118.
[35]董兴杰,周晓玲,朱赛霞.冰蓄冷空调负荷模糊预测原理及软件[J].制冷空调,2006,27(8)：22-25.
[36]陈柳,吴冬梅.空调负荷预测探讨[J].制冷,2007,26(3)：32-34.
[37]李琼,孟庆林,吉野博,等.基于支持向量机的建筑物空调负荷预测模型[J].暖通空调,2008,38(1)：14-18.
[38]Li Q, Meng Q L, Cai J J, et al. Applying support vector machine to predict hourly cooling load in the building [J]. Applied Energy,2009,86:2249-2256.
[39]Li Q, Meng Q L, Cai J J, et al. Predicting hourly cooling load in the building:A comparison of support vector machine and different artificial neural networks [J]. Energy Conversion and Management,2009, 50:90-96.
[40]Chow T T, Fong K F, Chan A L S, et al. Energy modeling of district cooling system for new urban development [J]. Energy and Buildings,2004,36:1153-1162.
[41]Yik F W H, Burnett J, Prescott I. Predicting air-conditioning energy consumption of a group of buildings using different heat rejection methods [J]. Energy and Buildings,2001,33:151-166.
[42]瞿燕.上海世博园区空调动态负荷预测与研究[J].暖通空调,2008,38(10)：9-17.
[43]Kosonen R, Shemeikka J. The use of a simple simulation tool for energy analysis[C]. Building Simulation 97, Fifth international IBPSA conference, Prague Czech Republic, vol.2. September 1997: 369-376.
[44]戴慎志.城市工程系统规划[M].北京：中国建筑工业出版社,1999.
[45]王耀春,陈汝冬.一种适合于冰蓄冷空调系统的全年逐时负荷计算方法[J].流体机械,2004,32(12)：69-71.
[46]苑翔,龙惟定.应用情景分析法预测区域建筑冷负荷[A].全国暖通空调制冷2008年学术年会资料集[C],2008.
[47]翟燕,潘毅群,黄治钟.上海世博园区空调负荷变化情景分析[J].建筑节能,2008,36(7)：12-17.
[48]中国城市规划设计研究院.GBJ 137-90城市用地分类与规划建设用地标准[S].北京：中国计划出版社,1991.
[49]钟新基,杨园英,何宜庚.土地与土地利用[M].北京：地震出版社,1994.
[50]厉美飞.典型办公室建筑空调设计负荷特性的模拟分析[D].重庆：重庆大学,2006.
[51]何天祺,牙侯专,张萍.办公建筑空调设计冷负荷相关因素的考察与分析[J].重庆建筑大学学报,1998,20(6)：56-60.
[52]董智慧,徐书朋,刘凡.建筑形状比对办公建筑冷(热)负荷的影响分析[J].节能,2008,(3)：35-37.
[53]董智慧,刘凡,庞俊香.建筑窗墙比对办公建筑冷(热)负荷的影响分析[J].建筑节能,2008,36(3)：6-9.
[54]Depecker P, Menezo C, Virgone J, et al. Design of buildings shape and energetic consumption [J]. Building and Environment,2001, (36):627-635.
[55]Ramzi O, Adnan A, Moncef K. A simplified analysis method to predict the impact of shape on annual energy use for office building [J]. Energy Conversion and Management,2007, (48):300-305.
[56]Adnan A, Donghyun S, Moncef K. Impact of building on thermal performance of office building in Kuwait [J]. Energy Conversion and Management,2009,50:822-828.
[57]姜益强,张志强,姚杨,等.用EnergyPlus模拟检验影响节能办公建筑的因素[J].建筑科学,2006,22(6)：22-26.
[58]苑翔,龙惟定.区域建筑冷负荷预测中的朝向因素分析与转换[J].土木建筑与环境工程,2009,31(6)：96-101.
[59]滕素珍,冯敬海.数理统计学[M].大连：大连理工大学出版社,2005.
[60]中国有色工程设计研究总院.GB 50019—2003采暖通风与空气调节设计规范[S].北京：中国计划出版社,2004.
[61]中国建筑科学研究院.GB 50189—2005公共建筑节能设计标准[S].北京：中国建筑工业出版社,2004.
[62]赵坚,刘金祥.基于BIN法的空调建筑全年能耗影响因素分析[J].节能技术,2007,25(4)：317-320.
[63]田胜元,萧日嵘.试验数据处理与分析[M].北京：中国建筑工业出版社,1988.
[64]何少华,文竹青,娄涛.试验设计与数据处理[M].湖南：国防科技大学出版社,2002.
[65]王立雄.建筑节能[M].北京：中国建筑工业出版社,2009.
[66]李良,杨文斌,韩春凤.民用建筑最佳节能体型的研究[J].工业建筑,2000,30(8)：21-22.
[67]曹毅然,陆善后,范宏武,等.建筑物体形系数与节能关系的探讨[J].住宅科技,2005,(4)：26-28.
[68]邹洪波.建筑节能体形的优化设计研究[J].建筑节能,2007,35(11)：26-27.
[69]王朋.建筑空调负荷动态计算分析[D].上海：上海交通大学,2007.
[70]Rees S.J, Spitler J.D, Haves P. Quantitative Comparison of North American and U.K.Cooling Load Calculation Procedures—Results [J]. ASHRAE Transactions,1998,104(2):47-61.
[71]谷波.建筑物空调负荷计算分析[M].北京：科学出版社,2003.
[72]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,2002.
[73]邓聚龙.灰理论基础[M].武汉：华中科技大学出版社,2002.
[74]王明明,赵宝元,刘小峰.运筹与决策基础[M].北京：中国林业出版社,2001.
[75]朱孔来.灰色马尔科夫链预测模型及其应用[J].系统工程理论与实践,1999,2：33-37.
[76]王露璐,邓丽,刘先涛.股票投资价值灰色马尔可夫预测[J].商业研究,2004,285(01)：137-139.
[77]陈赣洪.灰色马尔柯夫模型在保险预测中的应用[J].统计与预测,1994,13(02)：35-37.
[78]刘伟洁,詹棠森.灰色马尔可夫链在物流总额预测中的研究应用[J].办公自动化,2009,166(10)：25-26.
[79]叶学军,杨应玖.二次参数拟合灰色马尔科夫链商品房价格预测模型[J].水利电力将科技,1994,21(04)：72-76.
[80]安春晖,王建文.火灾事故的灰色马尔柯夫模型预测[J].消防科学与技术,2008,27(05)：367-370.
[81]张启明.灰色马尔可夫预测模型及在地震预报中的应用[J].地震科学,1994,25(01)：25-29.
[82]钱卫东,刘志强.基于灰色马尔可夫的道理交通事故预测[J].中国安全科学学报,2008,18(03)：33-36.
[83]陈水校.粘虫发生量的灰色马尔柯夫链预测[J].科技通报,1991,7(03)：150-152.
[84]戴淼,向亚丽.灰色马尔可夫模型的建设用地预测[J].国土资源科技管理,2007,24(04)：101-103.
[85]冯江浪.改进灰色马尔科夫模型及其在水资源预测中的应用[J].物探化探计算技术,2010,32(01)：109-112.
[86]石扬.基于灰色马尔可夫模型的能耗预测[J].山东轻工业学院学报,2009,23(02)：63-65.
[87]耿波,王君杰,张谢东.桥梁技术状况预测的灰色马尔科夫链模型研究[J].武汉理工大学学报,2007,31(01)：107-110.
[88]Scherer W T, Glagola D M. An overview of Markovian models fot bridge management system[C]. Proceedings of the ASCE conference on Infrastructure Planning and Management,1993:382-386.
[89]Kleiner Y. Scheduling inspection and renewal of large infrastructure assets[J]. Infrastructure System, 20017(04):136-143.
[90]Abraham D M, Wirahadikusuamh R, Short T J, et al. Optimization modeling for sewer network management [J]. Construction Engineering and Management,1998,124(05):402-410.
[91]李艳昌.基于灰色马尔科夫的残差蚁群灰色模型在负荷预测中的应用[J].华东电力,2007,35(07)：602-605.
[92]李芳.灰色理论在负荷预测中的应用[J].硅谷,2008,7(01)：12-13.
[93]吉培荣,邹红波,张玉文.无偏灰色预测模型在电力系统负荷预测中的应用[J].三峡大学学报(自然科学版),2005,27(04)：318-320.
[94]王白玲.电力负荷组合预测的理论方法及影响因素分析[D].北京：华北电力大学,2005.
[95]李恒.基于马尔可夫理论埋地燃气钢管腐蚀预测研究[D].大连：大连理工大学,2007.
[96]邓聚龙.灰色系统(社会经济)[M].北京：国防工业出版社,1983.
[97]李骥,邹瑜,谓峥.建筑能耗模拟软件的特点及应用中存在的问题[J].建筑科学,2010,26(02)：24-28.
[98]苏华.建筑物动态能耗分析用气象仿真模型研究[D].重庆：重庆大学,2002.
[99]吕政.建筑围护能耗的计算机仿真设计介绍[J].制冷空调与电力机械,2006,27(6)：30-34.
[100]Jiang Y. State-space method for analysis of the thermal behavior of room and calculation of air conditioning load[J]. ASHRAE Trans,1982,88:122-132.
[101]刘迎云.户式中央空调系统的经济性研究[D].湖南：湖南大学,2001.
[102]黑龙江省建筑设计院.JBJ 39—87托儿所、幼儿园建筑设计规范[S].北京：中国建筑工业出版社,1988.
[103]张宗尧,赵秀兰.托幼中小学校建筑设计手册[M].北京：中国建筑工业出版社,1999.
[104]天津市建筑设计院.GBJ 99—86中小学校建筑设计规范[S].北京：中国计划出版社,1987.
[105]中华人民共和国教育部.城市普通中小学校校舍建设标准[S].北京：高等教育出版社,2002.
[106]中华人民共和国国家教育委员会.普通高等学校建筑规划面积指标[M].北京：高等教育出版社,1992.
[107]西安建筑科技大学.JBJ 76—2003特殊教育学院建筑设计规范[S].北京：中国建筑工业出版社,2004.
[108]中国勘察设计协会技术经济委员会.民用建筑规划设计定额指标[M].北京：中国计划出版社,1995.
[109]上海市民用建筑设计院.JBJ 49—88综合医院建筑设计规范[S].北京：中国建筑工业出版社,1989.
[110]中华人民共和国卫生部.综合医院建设标准建标110-2008[M].北京：中国计划出版社,2008.