【《城市尺度建筑能耗特征及研究途径综述》4500字】

城市尺度建筑能耗特征及研究途径综述目录TOC\o"1-3"\h\u20216城市尺度建筑能耗特征及研究途径综述 1256551.1城市尺度建筑能耗特征 143101.2城市尺度建筑能耗研究方法 2104531.3城市尺度建筑能耗模型 31.1城市尺度建筑能耗特征（1）建筑群体互联城市中的建筑通常很难被完全剥离为“单体建筑”，不同功能的建筑通常是直接相连或以其他方式连接在一起[[]田玮,魏来,朱丽,何成,孙禹,杨松.城市规模的建筑能耗研究综述[J].建筑节能,2016,44(02):59-64.]。一次性建造的街区中，建筑的风格、材质、体块大小以及构造形式通常是一致的，但内部空间的使用方式和功能却可能完全不同，例如城市中最常见的商住混合建筑[[]田玮,魏来,朱丽,何成,孙禹,杨松.城市规模的建筑能耗研究综述[J].建筑节能,2016,44(02):59-64.[]HarryRBruhns,PhilipSteadman,HoraceHerring,SarahMoss,PeterARickaby.Types,Numbers,andFloorAreasofNondomesticPremisesinEnglandandWales,ClassifiedbyActivity[J].EnvironmentandPlanningB:PlanningandDesign,2000,27(5).（2）能耗波动放缓建筑单体能耗通常会表现出较为明显的峰谷交替变化规律，但即使是同一类建筑，其能耗也会由于建筑结构、建筑材质、设备和使用者社会特征等呈现显著的尖峰负荷差异，这种差异甚至会高达数倍之多[[]周欣,燕达.区域建筑负荷特性及影响因素分析[J].暖通空调,2014,44

(S1):264-269.][]周欣,燕达.区域建筑负荷特性及影响因素分析[J].暖通空调,2014,44

(S1):264-269.[]周欣,燕达.区域建筑负荷特性及影响因素分析[J].暖通空调,2014,44

(S1):264-269.（3）环境交互影响当前研究已充分证明，城市尺度的建筑能耗不仅取决于建筑单体的能耗水平，建筑物周围的环境也会影响建筑物的制冷和取暖能耗[[]Young-EunWoo,Gi-HyougCho.ImpactoftheSurroundingBuiltEnvironmentonEnergyConsumptioninMixed-UseBuilding[J].Sustainability,2018,10(3).]。建筑能耗与环境的交互影响在宏观、中观和微观层面均有所体现。宏观层面城市整体建筑能源消耗的增加导致城市温室气体排放的增加，进一步加剧全球变暖的趋势[[]《BP世界能源展望（2020）》/zh_cn/china/home.html[2020-10-30]]（图2-2）。就中国而言，建筑能耗占全国能源消费的20.6%，碳排放总量整体呈现出持续增长趋势，2016年达到19.61亿吨，较2000年6.68亿吨增长了约3倍，年均增长6.96%[[]蔡伟光.中国建筑能耗研究报告（2018）[R].中国建筑节能协会能耗统计专委会.上海.2020.]（图2-3）。研究报告显示，从20世纪初开始至今，地球表面的平均温度增加了约0.6℃；在过去的40年中，平均气温上升约0.2-0.3℃。气温的上升又进一步提升了城市整体的制冷需求，进而形成一种恶性循环。在中观层面，城市中心区的热岛效应使得中心区比城市边缘区域气温高[[]FedericoRossi,EmanueleBonamente,AndreaNicolini,ElisabettaAnderini,FrancoCotana.AcarbonfootprintandenergyconsumptionassessmentmethodologyforUHI-affectedlightingsystemsinbuiltareas[J].Energy&Buildings,2016,114.]，建筑制冷能耗更高。同时，区域人口和经济发展与建筑能耗也存在互相影响和制约关系。城市人口高密地区和生活水平较高地区，建筑能耗的峰值通常也较高。在微观层面，建筑与环境的交互影响主要体现在城市形态和微气候环境两方面。城市形态变量决定建筑物周围的气流[[]Young-EunWoo,Gi-HyougCho.ImpactoftheSurroundingBuiltEnvironmentonEnergyConsumptioninMixed-UseBuilding[J].Sustainability,2018,10(3).[]《BP世界能源展望（2020）》/zh_cn/china/home.html[2020-10-30][]蔡伟光.中国建筑能耗研究报告（2018）[R].中国建筑节能协会能耗统计专委会.上海.2020.[]FedericoRossi,EmanueleBonamente,AndreaNicolini,ElisabettaAnderini,FrancoCotana.AcarbonfootprintandenergyconsumptionassessmentmethodologyforUHI-affectedlightingsystemsinbuiltareas[J].Energy&Buildings,2016,114.[]K.Javanroodi,V.M.Nik,Interactionsbetweenextremeclimateandurbanmorphology:Investigatingtheevolutionofextremewindspeedsfrommesoscaletomicroscale,UrbanClimate,31(2020)100544.\t":8080/rwt/ELSEVIER/https/P75YPLUUMNVXK5UDMWTGT6UFMN4C6Z5QNF/science/article/pii/_blank"\o"使用数字对象标识符的持久链接"/10.1016/j.uclim.2019.100544.[]H.Takebayashi,E.Ishii,M.Moriyama,A.Sakaki,S.Nakajima,H.Ueda,Studytoexaminethepotentialforsolarenergyutilizationbasedontherelationshipbetweenurbanmorphologyandsolarradiationgainonbuildingrooftopsandwallsurfaces,SOLARENERGY,119(2015)362-369.\t":8080/rwt/ELSEVIER/https/P75YPLUUMNVXK5UDMWTGT6UFMN4C6Z5QNF/science/article/pii/_blank"\o"使用数字对象标识符的持久链接"/10.1016/j.solener.2015.05.039.[]V.Dorer,J.Allegrini,K.Orehounig,P.Moonen,Modellingtheurbanmicroclimateanditsimpactontheenergydemandofbuildingsandbuildingclusters,ProceedingsofBS2013:13ThConferenceoftheInternationalBuildingPerformanceSimulationAssociation(2013)3483-3489.图2-2一次能源耗能终端占比Fig.2-2Primaryenergyconsumptionbyend-usesector图2-3我国建筑能耗及碳排放年均增速Fig.2-3TheaverageannualgrowthrateofbuildingenergyconsumptionandcarbonemissioninChina1.2城市尺度建筑能耗研究方法城市尺度建筑能耗的研究是一项庞大、复杂且艰巨的任务，影响因素众多且并非单一单向影响，影响因素之间的互相作用也因研究尺度的扩大而变化，均提升了研究的困难程度。但总体来讲，与单体建筑能耗研究不同，城市尺度建筑能耗研究通常将研究范围内的对象看做一个整体，以系统性思维进行全局探讨。基于YixuanWei等人的归类，目前城市尺度建筑能耗的研究方法可大致分为“white-box（白盒）”、“gray-box（灰盒）”和“black-box（黑盒）”三类[[][]YixuanWei,XingxingZhang,YongShi,LiangXia,SongPan,JinshunWu,MengjieHan,XiaoyunZhao.Areviewofdata-drivenapproachesforpredictionandclassificationofbuildingenergyconsumption[J].RenewableandSustainableEnergyReviews,2018,82.表2-1城市尺度建筑能耗研究方法分类Table2-1Classificationofresearchmethodsforbuildingenergyconsumptiononurbanscale方法基础信息输入方法难易度速度准确性white-box（白盒）详细模拟详细的建筑物理信息，天气数据DOE-2、EnergyPlus、TRYSYS、ESP-r难慢高单一模拟度日法、温度频率法、住宅负荷系数法易快较高grey-box（灰盒）建筑物理信息，能耗历史数据，天气数据贝叶斯网络法、RC网络法难慢较高black-box（黑盒）建筑类型，能耗历史数据，天气数据，使用者行为数据，设备属性人工神经网络(ANN)、箱法(BM)、支持向量回归(SVR)、多元线性回归(MLR)、高斯回归(GPR)难快（除“支持向量机”外）高（除“统计回归”外）白盒方法最早用于建筑单体能耗的研究中，基于详细的建筑物理信息来推测建筑用能情况，分为详细模拟和单一模拟两类。其中，详细模拟使用的技术手段，例如EnergyPlus和DOE-2等模拟技术平台作为建筑单体能耗模拟的基础被广泛应用。但对于城市尺度而言，巨量的建筑信息会带来超负荷的计算，导致模拟过程过长。同时，校准详细模拟的过程也较为困难，可用的校准数据通常较少，且不确定的参数输入也会影响模拟结果的准确性[[]ZhengweiLi,YanminHan,PengXu.Methodsforbenchmarkingbuildingenergyconsumptionagainstitspastorintendedperformance:Anoverview[J].AppliedEnergy,2014,124.]。单一模拟的白盒方法相对详细模拟更为简单，耗时更短，通常基于一定的数学模型计算得到合理能耗水平推测。度日法是其中应用较广的一种方法，主要指制冷度日数（CDD）和采暖度日数（HDD）。该方法主要通过计算室外逐日平均温度高（低）于温度基数的度数之和来衡量外界气温对建筑能源需求的影响[[][]ZhengweiLi,YanminHan,PengXu.Methodsforbenchmarkingbuildingenergyconsumptionagainstitspastorintendedperformance:Anoverview[J].AppliedEnergy,2014,124.[]ChinaAssociationofBuildingEnergyEfficiency,“ChinaBuildingEnergyConsumptionResearchReport,”(Shanghai,China:ChinaAssociationofBuildingEnergyEfficiency,2018),1-36./upload/file/20181229/1546072726524234.pdf.[]SathayeJA,DaleLL,LarsenPH,etal.EstimatingimpactsofwarmingtemperaturesonCalifornia’selectricitysystem.GlobalEnvironmentalChange,2013,23(2):499-511.灰盒方法是在白盒方法的基础上增加建筑用电能耗的历史数据，以此为基础通过统计学方法进行建筑能耗预测的一种方法。贝叶斯网络法和RC网络法是主要的两种方法。贝叶斯网络适用于大规模地对建筑能耗进行基准测试，尤其适用于缺少参数或能耗监测数据的情况。RC网络多用于模拟空调负载[[]WangS,XuX.Simplifiedbuildingmodelfortransientthermalperformances

estimationusingGA-basedparameteridentification.IntJThermSci

[]WangS,XuX.Simplifiedbuildingmodelfortransientthermalperformances

estimationusingGA-basedparameteridentification.IntJThermSci

2006;45:419–32.[]BraunJE,ChaturvediN.Aninversegray-boxmodelfortransientbuildingload

prediction.HVAC&RRes2002;8:73–99.[]LeeK,BraunJE.Developmentofmethodsfordeterminingdemand-limiting

setpointtrajectoriesinbuildingsusingshort-termmeasurements.Build

Environ2008;43:1755–68.黑盒方法近些年逐渐受到学界关注，在不断的发展和校准下，此方法的优势在大规模建筑能耗研究中开始凸显。人工神经网络源自神经生物学，目前已被广泛用于预测取暖、制冷和总能耗[[]LiZ,HuangG.Re-evaluationofbuildingcoolingloadpredictionmodelsforuse

inhumidsubtropicalarea.EnergyBuild2013;62:442–9.][[]LiQ,MengQ,CaiJ,etal.Predictinghourlycoolingloadinthebuilding:a

comparisonofsupportvectormachineanddifferentartificialneuralnetworks.

EnergyConversManage2009;50:90–6.[]LiZ,HuangG.Re-evaluationofbuildingcoolingloadpredictionmodelsforuse

inhumidsubtropicalarea.EnergyBuild2013;62:442–9.[]LiQ,MengQ,CaiJ,etal.Predictinghourlycoolingloadinthebuilding:a

comparisonofsupportvectormachineanddifferentartificialneuralnetworks.

EnergyConversManage2009;50:90–6.[]AydinalpM,UgursalVI,FungAS.Modelingofthespaceanddomestichotwaterheatingenergy-consumptionintheresidentialsectorusingneural

networks.ApplEnergy2004;79:159–78.[]MihalakakouG,SantamourisM,TsangrassoulisA.Ontheenergyconsumption

inresidentialbuildings.EnergyBuild2002;34:727–36.[]KaratasouS,SantamourisM,GerosV.Modelingandpredictingbuilding’s

energyusewithartificialneuralnetworks:methodsandresults.EnergyBuild

2006;38:949–58.[]ManfrenM,AsteN,MoshksarR.Calibrationanduncertaintyanalysisfor

computermodels–ameta-modelbasedapproachforintegratedbuilding

energysimulation.ApplEnergy2013;103:627–41.1.3城市尺度建筑能耗模型城市尺度建筑能耗模拟建模最基本的原理是基于某种数学模型进行计算，包含输入变量、系统结构和特性、输出变量三个部分。通常天气参数、人群用能参数等为输入变量，建筑本身的物理特性、围护结构、空调系统等为系统结构和特性，而最终的能耗结果为输出变量。在建筑能耗情景模拟中，采用这种思路，控制输入变量，设定系统结构和特性，得到目标能耗量结果。除数据收集与处理外，跨学科技术支持也是此类研究的一大挑战。与单体建筑节能研究相比，城市尺度建筑能耗研究要复杂的多。在城市空间环境中，建筑与其所处的周边空间环境的关系会对能耗表现产生较大的影响[[]YANGX,ZHAOL,BRUSEM,etal.Anintegratedsimulationmethodfor

buildingenergyperformanceassessmentinurbanenvironments[J].Energy

andbuildings,2012,54(6):243-251.][[]GUATTARIC,EVANGELISTIL,BALARASCA.Ontheassessment

ofurbanheatislandphenomenonanditseffectsonbuildingenergy

performance:acasestudyofRome(Italy)[J].Energyandbuildings,2017,

158:605-615.]，仅依靠理论研究是远远不够的。在过去的几年内，多学科交叉的研究方法已逐渐成为规划研究领域的一种趋势，随着地理信息系统（GIS）[[]WANGH,SHENQ,TANGBS.GIS-basedframeworkforsupportingland

useplanninginurbanrenewal:casestudyinHongKong[J].Journalofurban

planninganddevelopment,2014,141(3):05014015.]，贝叶斯模型（BayesMethod）[[]MEYERSR,JOHNSONML,LILIEHOLMRJ,etal.Developmentof

astakeholder-drivenspatialmodelingframeworkforstrategiclandscape

planningusingBayesiannetworksacrosstwourban-ruralgradientsinMaine,

USA[J].Ecologicalmodelling,2014,291:42-57.]，[]YANGX,ZHAOL,BRUSEM,etal.Anintegratedsimulationmethodfor

buildingenergyperformanceassessmentinurbanenvironments[J].Energy

andbuildings,2012,54(6):243-251.[]GUATTARIC,EVANGELISTIL,BALARASCA.Ontheassessment

ofurbanheatislandphenomenonanditseffectsonbuildingenergy

performance:acasestudyofRome(Italy)[J].Energyandbuildings,2017,

158:605-615.[]WANGH,SHENQ,TANGBS.GIS-basedframeworkforsupportingland

useplanninginurbanrenewal:casestudyinHongKong[J].Journalofurban

planninganddevelopment,2014,141(3):05014015.[]MEYERSR,JOHNSONML,LILIEHOLMRJ,etal.Developmentof

astakeholder-drivenspatialmodelingframeworkforstrategiclandscape

planningusingBayesiannetworksacrosstwourban-ruralgradientsinMaine,

USA[J].Ecologicalmodelling,2014,291:42-57.[]LEVYS,MARTENSK,HEIJDENRVD.Agent-basedmodelsandselforganization:addressingcommoncriticismsandtheroleofagent-based

modellinginurbanplanning[J].Townplanningreview,2016,87(3):321-

338.[]ROBINSONC,DILKINAB,HUBBSJ,etal.Machinelearningapproaches

forestimatingcommercialbuildingenergyconsumption[J].Appliedenergy,

2017,208:889-904[]SEYEDZADEHS,RAHIMIANFP,GLESKI,etal.Machinelearningfor

estimationofbuildingenergyconsumptionandperformance:areview[J].

Visualizationinengineering,2018,6:5.DOI:/s40327-018-

0064-7[]DENGH,FANNOND,ECKELMANMJ.PredictivemodelingforUS

commercialbuildingenergyuse:acomparisonofexistingstatisticaland

machinelearningalgorithmsusingCBECSmicrodata[J].Energyand

buildings,2018,163:34-43.[]SWANLG,UGURSALVI.Modelingofend-useenergyconsumptionin

theresidentialsector:areviewofmodelingtechniques[J].Renewableand

sustainableenergyreviews,2009,13(8):1819-1835.自上而下的模型主要应用于大尺度区域对建筑总体能耗的研究，主要考虑宏观层面影响因素[[]GUATTARIC,EVANGELISTIL,BALARASCA.Ontheassessment

ofurbanheatislandphenomenonanditseffectsonbuildingenergy

performance:acasestudyofRome(Italy)[J].Energyandbuildings,2017,

158:605-615]。自上而下模型将城市或区域内的所有建筑作为一个整体[[]FRAYSSINETL,MERLIERL,KUZNIKF,etal.Modelingtheheatingand

coolingenergydemandofurbanbuildingsatcityscale[J].Renewableand

sustainableenergyreviews,2017,81(2):2318-2327][[]GUATTARIC,EVANGELISTIL,BALARASCA.Ontheassessment

ofurbanheatislandphenomenonanditseffectsonbuildingenergy

performance:acasestudyofRome(Italy)[J].Energyandbuildings,2017,

158:605-615[]FRAYSSINETL,MERLIERL,KUZNIKF,etal.Modelingtheheatingand

coolingenergydemandofurbanbuildingsatcityscale[J].Renewableand

sustainableenergyreviews,2017,81(2):2318-2327[]KAVGICM,MAVROGIANNIA,MUMOVICD,etal.Areviewofbottomupbuildingstockmodelsforenergyconsumptionintheresidentialsector[J].

Buildingandenvironment,2010,45(7):1683-1697.[]LIUY,LIUT,YES,etal.Cost-benefitanalysisforenergyefficiencyretrofit

ofexistingbuildings:acasestudyinChina[J].Journalofcleanerproduction,

2017,177:493-506[]GUATTARIC,EVANGELISTIL,BALARASCA.Ontheassessment

ofurbanheatislandphenomenonanditseffectsonbuildingenergy

performance:acasestudyofRome(Italy)[J].Energyandbuildings,2017,

158:605-615经济模型主要着眼于与宏观层面与经济发展相关的城市尺度建筑能耗问题，建模数据主要来自城市经济发展数据，如能源价格、人均GDP等。伯内特（Burnett）等人基于经济数据构建了一个自上而下的建筑能耗模型，通过分析社会经济要素、用能行为和物质空间因素的关系，探讨这三类因素如何影响住宅能源消耗情况[[]BURNETTJW,MADARIAGAJ.Atop-downeconomicefficiencyanalysis

ofU.S.householdenergyconsumption[J].Theenergyjournal,2017,39:4]。技术模型通常基于历史能耗数据，构建多参数多变量函数，通过预测在某种变量改变的前提下未来整体建筑能源消耗情况[[][]BURNETTJW,MADARIAGAJ.Atop-downeconomicefficiencyanalysis

ofU.S.householdenergyconsumption[J].Theenergyjournal,2017,39:4[]LIUY,LIUT,YES,etal.Cost-benefitanalysisforenergyefficiencyretrofit

ofexistingbuildings:acasestudyinChina[J].Journalofcleanerproduction,

2017,177:493-506.[]URQUIZOJ,CALDERÓNC,JAMESP.Understandingthecomplexitiesof

domesticenergyreductionsincities:integratingdatasetsgenerallyavailable

intheUnitedKingdom’slocalauthorities[J].Cities,2018,74:292-307.图2-4城市尺度建筑能耗模拟模型分类Fig.2-4Classificationofurbanscalebuildingenergyconsumptionsimulationmodels在城市尺度建筑节能规划研究中，相较于自上而下模型而言，自下而上模型受到更多的关注。自下而上模型通常采用微观视角来估算整体城市尺度建筑能耗[50][51]，分为工程模型和统计模型。工程模型需要以详细的城市尺度建筑能耗数据和建筑群体物理空间数据为基础，此类模型可以模拟随着居民能源使用行为及其他影响要素变化而产生的能耗变化[[]TIANW,CHOUDHARYR.Aprobabilisticenergymodelfornon-domestic

buildingsectorsappliedtoanalysisofschoolbuildingsinGreaterLondon[J].

Energyandbuildings,2012,54(6):1-11.][[]CORGNATISP,FABRIZIOE,FILIPPIM,etal.Referencebuildingsfor

costoptimalanalysis:methodofdefinitionandapplication[J].Applied

energy,2013,102:983-993][[]CHENGV,STEEMERSK.Modellingdomesticenergyconsumption

atdistrictscale:atooltosupportnationalandlocalenergypolicies[J].

Environmentalmodellingandsoftware,2011,26(10):1186-1198.]。巴拉里尼（Ballarini）等人提出了一种统一划分“欧洲建筑类型”的方法，并以意大利国家建筑能耗数据库为基础进行研究，从而构建原模型[[]BALLARINII,CORGNATISP,CORRADOV.Useofreferencebuildings

toassesstheenergysavingpotentialsoftheresidentialbuildingstock:the

experienceofTabulaproject[J].Energypolicy,2014,68:273-284.]。卡扎斯（Kazas）等人采用样本建模法设计了一种名为DiDeProM的自下而上模型，分析识别影响城市尺度建筑能耗的重要变量[[]KAZASG,FABRIZIOE,PERINOM.Energydemandprofilegeneration

withdetailedtimeresolutionatanurbandistrictscale:areferencebuilding

approachandcasestudy[J].Appliedenergy,2017,193:243-262.]。卡迪安（Kadian）等人对能耗终端设备使用情况进行情景模拟建模，并依据结果提出一项长期的能源替代计划（LEAP）[[]TIANW,CHOUDHARYR.Aprobabilisticenergymodelfornon-domestic

buildingsectorsappliedtoanalysisofschoolbuildingsinGreaterLondon[J].

Energyandbuildings,2012,54(6):1-11.[]CORGNATISP,FABRIZIOE,FILIPPIM,etal.Referencebuildingsfor

costoptimalanalysis:methodofdefinitionandapplication[J].Applied

energy,2013,102:983-993[]CHENGV,STEEMERSK.Modellingdomesticenergyconsumption

atdistrictscale:atooltosupportnationalandlocalenergypolicies[J].

Environmentalmodellingandsoftware,2011,26(10):1186-1198.[]BALLARINII,CORGNATISP,CORRADOV.Useofreferencebuildings

toassesstheenergysavingpotentialsoftheresidentialbuildingstock:the

experienceofTabulaproject[J].Energypolicy,2014,68:273-284