ENERGY ASSESSMENT OF URBAN BUILDINGS BASED ON GEOGRAPHIC INFORMATION SYSTEM

Urban building energy analysis has attracted more attention as the population living in cities increases as does the associated energy consumption in urban environments.This paper proposes a systematic bottom-up method to conduct energy analysis and assess energy saving potentials by combining dynamic engineering-based energy models,machine learning models,and global sensitivity analysis within the GIS(Geographic Information System)environment for large-scale urban buildings.This method includes five steps:database construction of building parameters,automation of creating building models at the GIS environment,construction of machine learning models for building energy assessment,sensitivity analysis for choosing energy saving measures,and GIS visual evaluation of energy saving schemes.Campus buildings in Tianjin(China)are used as a case study to demonstrate the application of the method proposed in this research.The results indicate that the method proposed here can provide reliable and fast analysis to evaluate the energy performance of urban buildings and determine effective energy saving measures to reduce energy consumption of urban buildings.Moreover,the GIS-based analysis is very useful to both create energy models of buildings and display energy analysis results for urban buildings.

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities:Current trends and future outlook

Energy demand fluctuations due to low probability high impact(LPHI)micro-climatic events such as urban heat island effect(UHI)and heatwaves,pose significant challenges for urban infrastructure,particularly within urban built-clusters.Mapping short term load forecasting(STLF)of buildings in urban micro-climatic setting(UMS)is obscured by the complex interplay of surrounding morphology,micro-climate and inter-building energy dynamics.Conventional urban building energy modelling(UBEM)approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale.Reduced order modelling,unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization,limit the inter-building energy dynamics consideration into UBEMs.In addition,mismatch of resolutions of spatio-temporal datasets(meso to micro scale transition),LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations.This review aims to direct attention towards an integrated-UBEM(i-UBEM)framework to capture the building load fluctuation over multi-scale spatio–temporal scenario.It highlights usage of emerging data-driven hybrid approaches,after systematically analysing developments and limitations of recent physical,data-driven artificial intelligence and machine learning(AI-ML)based modelling approaches.It also discusses the potential integration of google earth engine(GEE)-cloud computing platform in UBEM input organization step to(i)map the land surface temperature(LST)data(quantitative attribute implying LPHI event occurrence),(ii)manage and pre-process high-resolution spatio-temporal UBEM input-datasets.Further the potential of digital twin,central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored.It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.

The potential of remote sensing and GIS in urban building energy modelling

As the world continues to urbanize at an unprecedented rate,the energy demand in cities is rising.Buildings account for over 75%of all the energy consumed in cities and are responsible for over two-thirds of the emissions.Assessment of energy demand in buildings is a highly integrative endeavour,bringing together the interdisciplinary fields of energy and urban studies,along with a host of technical domains namely,geography,engineering,economics,sociology,and planning.In the last decade,several urban building energy modelling tools(UBEMs)have been developed for estimation as well as prediction of energy demand in cities.These models are useful in policymaking as they can evaluate future urban energy scenarios.However,data acquisition for generating the input database for UBEM has been a major challenge.In this review,a comprehensive assessment of the potential of remote sensing and GIS techniques for UBEM has been presented.Firstly,the most common input variables of UBEM have been identified by reviewing recent publications on UBEM and then studies related to the acquisition of data corresponding to these variables have been explored.More than 140 research papers and review articles relevant to remote sensing and GIS applications for building level data extraction in urban areas and UBEM applications have been investigated for this purpose.After going through level of details required for each of the input components of UBEM and studying the possibility of acquiring some of those data using remote sensing,it has been inferred that satellite remote sensing and Unmanned Aerial Vehicles(UAVs)have a strong potential in enhancing the input data space for UBEM but their applicability has been limited.Further,the challenges of the usage of these technologies and the possible solutions have also been presented in this study.It is recommended to utilise the existing methodologies of extracting information from remote sensing and GIS for UBEM,along with newer techniques such as machine learning and artificial intelligence.

Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets

Urban building energy modeling has become an efficient way to understand urban building energy use and explore energy conservation and emission reduction potential.This paper introduced a method to identify archetype buildings and generate urban building energy models for city-scale buildings where public building information was unavailable.A case study was conducted for 68,966 buildings in Changsha city,China.First,clustering and random forest methods were used to determine the building type of each building footprint based on different GIS datasets.Then,the convolutional neural network was employed to infer the year built of commercial buildings based on historical satellite images from multiple years.The year built of residential buildings was collected from the housing website.Moreover,twenty-two building types and three vintages were selected as archetype buildings to represent 59,332 buildings,covering 87.4%of the total floor area.Ruby scripts leveraging on OpenStudio-Standards were developed to generate building energy models for the archetype buildings.Finally,monthly and annual electricity and natural gas energy use were simulated for the blocks and the entire city by EnergyPlus.The total electricity and natural gas use for the 59,332 buildings was 13,864 GWh and 23.6×10^(6) GJ.Three energy conservation measures were evaluated to demonstrate urban energy saving potential.The proposed methods can be easily applied to other cities in China.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization.Urban building energy modeling(UBEM)is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations,supporting the implementation of carbon emission reduction policies.Currently,most studies focus on the energy performance of archetype buildings under climate change,which is hard to obtain refined results for individual buildings when scaling up to an urban area.Therefore,this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas,by taking two urban neighborhoods comprising 483 buildings in Geneva,Switzerland as case studies.In this regard,GIS datasets and Swiss building norms were collected to develop an archetype library.The building heating energy consumption was calculated by the UBEM tool—AutoBPS,which was then calibrated against annual metered data.A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%.The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).The results showed a decrease of 22%–31%and 21%–29%for heating energy consumption,an increase of 113%–173%and 95%–144%for cooling energy consumption in the two neighborhoods by 2050.The average annual heating intensity dropped from 81 kWh/m^(2) in the current typical climate to 57 kWh/m^(2) in the SSP5-8.5,while the cooling intensity rose from 12 kWh/m^(2) to 32 kWh/m^(2).The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7%and 18.6%,respectively,in the SSP scenarios.The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

Ranking parameters in urban energy models for various building forms and climates using sensitivity analysis

Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since the model requires overly detailed input data,which is not necessarily publicly unavailable.Model calibration is a necessary step to reduce the uncertainties and simulation results in order to develop a reliable and accurate UBEM.Due to the concerns over computational resources and the time needed for calibration,a sensitivity analysis is often required to identify the key parameters with the most substantial impact before the calibration is deployed in UBEM.Here,we study the sensitivity of uncertain input parameters that affect the annual heating and cooling energy demand by employing an urban-scale energy model,CitySim.Our goal is to determine the relative influence of each set of input parameters and their interactions on heating and cooling loads for various building forms under different climates.First,we conduct a global sensitivity analysis for annual cooling and heating consumption under different climate conditions.Building upon this,we investigate the changes in input sensitivity to different building forms,focusing on the indices with the largest Total-order sensitivity.Finally,we determine First-order indices and Total-order effects of each input parameter included in the urban building energy model.We also provide tables,showing the important parameters on the annual cooling and heating demand for each climate and each building form.We find that if the desired calibration process require to decrease the number of the inputs to save the computational time and cost,calibrating 5 parameters;temperature set-point,infiltration rate,floor U-value,avg.walls U-value and roof U-value would impact the results over 55%for any climate and any building form.

Influence of urban morphological factors on building energy consumption combined with photovoltaic potential: A case study of residential blocks in central China

Studies on urban energy have been growing in interest,and past research has mostly been focused on studies of urban solar potential or urban building energy consumption independently.However,holistic research on the combination of urban building energy consumption and solar potential at the urban block-scale is required in order to minimize energy use and maximize solar power generation simultaneously.The aim of this study is to comprehensively evaluate the impact of urban morphological factors on photovoltaic(PV)potential and building energy consumption.Firstly,58 residential blocks were classified into 6 categories by k-means clustering.Secondly,3 energy performance factors,which include the energy use intensity(EUI),the energy use intensity combined with PV potential(EUI-PV),and photovoltaic substitution rate(PSR)were calculated for these blocks.The study found that the EUI of the Small Length&High Height blocks was the lowest at around 30 kWh/(m^(2)·y),while the EUI-PV of the Small Length&Low Height blocks was the lowest at around 4.45 kWh/(m^(2)·y),and their PSR was the highest at 87%.Regression modelling was carried out,and the study concluded that the EUI of residential blocks was mainly affected by shape factor,building density and floor area ratio,while EUI-PV and PSR were mainly affected by height and sky view factor.In this study,the results and developed methodology are helpful to provide recommendations and strategies for sustainable planning of residential blocks in central China.

建筑物能量模式的改进及制冷系统人为热排放研究

针对近年来中国城市化进程不断加快,建筑物制冷系统的排热对城市气候的影响越来越大的现状,以2010年8月6 7日北京地区夏季典型晴天为例,开展了对建筑物能量模式(Building Energy M odel,BEM)和制冷系统人为热排放的研究。分析发现不同用途建筑物的用电量日变化特征不同,其与气象因子(主要是气温)之间存在一定的相关性。在此基础上,改进了BEM模式,并对制冷系统(空调)能耗和排热进行了模拟。首先,基于用电量日变化特点模拟不同用途建筑物的排热情况,表明在建筑物空调制冷系统负荷中,窗墙传热占60%以上,人员、设备产热占30%,通风设施传热占5%~6%;其次,对影响建筑物排热量较大的一些参数进行敏感性试验,建筑参数中建筑物高度对排热的影响最大,从18.3 m降低到12 m和6 m,排热量可分别减少24.3%和49.6%,紧随其后的是墙体传热系数和新风系数的影响,而空调设定参数中设定温度从25℃下降1℃,空调制冷系统排热猛增94.4%;最后,根据我国夏季各种类型空调占比情况,计算出空调排热中感热、潜热分别为12.69 W·m-2和45.87 W·m-2(约占22%和78%),为建筑物排热对城市气候影响研究奠定了基础。

基于地理信息系统的城市建筑能耗模型建立方法

为降低城市快速膨胀导致的能源消耗及温室气体总量,城市环境建筑能耗分析已经成为研究热点,但是城市环境中区域建筑能耗模型尚处于初步研究阶段。随着我国城市地理信息系统及相关数据库的完善,为建立准确且预测功能完善的建筑能耗模型提供了便利条件。重点探讨根据城市地理信息系统及相关统计数据,建立三维城市环境建筑动态能耗模型。重点分析了可能遇到的主要问题及解决方法,包括如何简化地理信息系统的数据、考虑城市建筑复杂性、模型建立的自动化完成、全局敏感性分析节能措施等。并且用2个实例说明处理空间规模不同的建筑群可采用的不同建模方法。研究不仅为城市节能提供指导,也为建筑信息模型的发展和促进城市规划领域中建筑能耗模型的广泛应用提供了坚实基础。

建筑物制冷系统人为热排放与气象环境的相互作用

针对建筑物制冷系统人为热排放对城市气候和能源消耗影响越来越大的现状,利用改进后的建筑物能量模式BEM(Building Energy Model)与单层城市冠层模式SLUCM(Single Layer Urban Canopy Model)的耦合,实现对城市建筑物人为热排放的动态模拟;以2014年5月29日为例(北京地区极端高温个例),开展北京地区建筑物制冷系统人为热排放与城市气象环境相互作用的定量分析。WRF(Weather Research and Forecasting)/Noah/SLUCM/BEM耦合模式模拟分析表明,模式在不加入人为热时,对夜间的热岛模拟偏弱,且基本无法模拟出白天的热岛效应;加入城市交通人为热排放后,对城市热岛强度和范围的模拟有一定改善;进一步加入建筑人为热排放对气温、热通量、边界层高度等的模拟效果均有不同程度的改进。加入BEM模拟的人为热后(case2),15:00(北京时,下同)主城区地表感热通量增加30~50 W·m^(-2),相应地2 m气温升高0.4~0.8℃,二者对应关系较好。case2中的人为潜热排放导致地表潜热通量增加80~140 W·m^(-2),水汽通量增加0.04~0.09 g·m^(-2)·s-1,中心城区2 m比湿增加0.5~0.9 g·kg^(-1),边界层高度升高100~150 m,且傍晚边界层高度开始下降的时间推迟了约1 h。加入建筑人为热后,气温等气象条件的变化会对建筑物制冷系统能耗及人为热排放产生影响。case2对比case1,建筑物制冷系统能耗增加了1.11%~3.33%,建筑物制冷系统排放的感热通量增大0.67%~1.67%、潜热通量增大0.625%~1.56%(达2.0 W·m^(-2)以上)。研究表明,在中尺度模式中动态模拟建筑物制冷系统的人为热排放,能够改进对近地层气象要素的模拟效果。

应用城市冠层模式对建筑物表面太阳辐射的分析

利用城市冠层模式对建筑物表面的太阳辐射进行模拟,并将模拟结果与部分观测作了对比,一致性较好。在此基础上重点研究由于建筑物之间的遮蔽效应和墙面间的反射作用对建筑物各墙面所接收到的太阳辐射的影响。结果表明:(1)冬季建筑物墙面之间的遮蔽效应会明显减少建筑物的太阳辐照度,而且在早晨和傍晚尤为明显,在实际对太阳能利用的过程中,应该考虑这种遮蔽效应,否则会高估壁面所获得的太阳能。墙面之间的反射会小幅增加壁面太阳辐照度10～20 W.m-2。(2)建筑物墙面间的遮蔽效应和反射辐射随建筑群的高度和密度的增加而增大,特别在冬季,高而密的城市建筑区域,由于墙面的遮蔽,会使得壁面的日照时数大大减少。(3)纬度能够影响墙面遮蔽时间和太阳辐射相对于墙面的倾斜角,冬季,纬度越高,南墙日照时数越小;夏季则反之。

Comparative study of development scenarios to decipher carbon emissions of new/old campuses in China with urban building energy model:A case study of Southeast University

The students receiving higher education boosted a total increase of 416.45%in China in last 20 years,resulting in newly built campuses reaching over 4.4 billion m^(2).Therefore,implementing low-carbon development on university campuses is an important part of achieving carbon neutrality in China.In this study,the old and new campuses of Southeast University in China were selected and the Rhino Grasshopper tool was used to create and calibrate their energy model with real electricity data to ensure the 20%error range.The calibrated energy model was used to set up four base scenarios under different development paths in year 2030 and 2050,including natural development,campus construction,policy-oriented,and sustainable development.The simulation indicates that campus construction leads to the greatest increase in carbon emissions,with the old campus and new campus experiencing a 16.7%and 162.9%rise,respectively,compared to the current situation.In contrast,policy-oriented scenarios result in the most significant reduction in emissions,decreasing by 121.4%and 114.5%for each scenario,respectively.Only policy-driven approaches will enable both campuses to achieve carbon neutrality by 2050.The driving factor decomposition analysis indicates that in no-policy-intervention scenarios,the primary contributors to carbon emissions are short-term climate fluctuations and aging equipment.Conversely,in scenarios with government intervention,the pivotal elements are the implementation of renewable energy and the development of low-carbon technologies.The results of the static scenario combination show that the old campus has a significant lower average carbon emission of 7,080 t than 279,090 t of the new campus in 2050.However,the new campus shows higher potential,with a proportion of 38.3%achieving carbon neutrality in the combination results,compared to 17.2%for the old campus.The study results offer insights into the pathway for universities to achieve carbon neutrality,emphasizing the significance of policy direction and the adoption of renewable energy.

城市建筑群碳排放核算模型构建与实证研究

近年来,为加强低碳城市建设、实现区域低碳发展,我国各级政府相继推出了诸如低碳社区试点、低碳城镇试点等城市建筑群的低碳示范试点。设置节能减碳目标、出台政策举措和评价试点成效的前提是对碳排放的定量评估。城市建筑群碳排放核算模型综合借鉴了IPCC方法学、《城市温室气体核算国际标准》(GPC)、WRI《城市温室气体核算工具(1.0、2.0)》和国家发改委《公共建筑运营单位(企业)温室气体排放核算方法和报告指南(试行)》等碳核算方法学,基于城市建筑群碳排放高度集聚、来源复杂、边界模糊等特征,对相关方法学进行了适用性修正,并通过某高教园区碳核算的实证检验。结果表明:城市建筑群碳排放模型可成为相关政府部门和管理机构核算碳排放的有效工具,为规划制定、目标设置、政策出台和成效评估提供了定量依据。

高反照率屋顶对城市热岛及空调能耗的影响

为了研究高反照率屋顶这一城市降温技术对夏季城市热岛效应的缓解作用及对空调制冷负荷的影响,以夏季高温频发的杭州市为例,选取2017年7月22日—28日作为典型高温时段,采用中尺度天气预报模式(WRF)耦合考虑建筑物能量交换的多层城市冠层参数化方案(BEP+BEM)进行数值模拟.结果表明,反照率为0.85的屋顶使城市区域日均降温0.37℃,城市热岛强度减小0.21℃,城市热岛效应得到了一定程度的缓解,空调制冷负荷降低约5.3%;建筑物密集的商业区的降温和节能效果均优于低密度居住区;屋顶反照率与城区2 m气温及空调能耗均线性负相关.

“低碳城市”理念下中国城市的发展模式转型

阐述了"低碳城市"的概念及发展要求,分析了中国城市发展模式的现状,并以"低碳城市"为核心理念,从能源、交通、建筑及环保四个方面探索了新的城市规划发展模式,从而为我国低碳城市发展提供了理论基础。

自下而上的城市建筑群能耗模型研究综述

城市建筑群能耗模型是分析城市区域建筑能耗水平的重要工具,为城市区域的节能策略制定提供了有力支持。城市建筑群能耗模型可以分为自上而下和自下而上两种,因为自下而上模型能够更清楚地表示出个体建筑与整体区域之间的关系,更适用于城市层面,所以重点回顾了自下而上模型研究的最新进展。针对统计、物理、混合等3种主要的自下而上模型,分别从研究方法、模拟工具、数据获取、优缺点等方面展开评述,并对未来的相关研究进行了展望。综述认为,未来物理模型依旧会是自下而上模型研究的主导,而混合模型的研究、公开可用的数据库建立、跨学科的研究方法融合会进一步促进该领域研究的发展。

基于GIS和历史卫星影像的城市建筑大数据识别

在城市建筑群能耗模拟中,建筑类型和建筑年代是典型建筑参考的主要依据,目前较难直接获取相关数据.为识别建筑类型,以长沙市区21538个建筑轮廓(不含城市地图信息点和区域边界轮廓信息)为例,基于建筑轮廓的轮廓面积、近似矩形短边宽度、近似矩形系数等几何特征,运用随机森林方法成功识别出低层住宅、公寓式住宅和其他类型,整体准确率为81.7%.为识别建筑年代,以长沙市中心区域7900个建筑轮廓为例,基于历史卫星影像数据,运用卷积神经网络方法自动提取不同年代的建筑轮廓,平均精确度为80%.然后分别相交分析推断出5077栋建筑建造于2005年之前,1606栋建筑建造于2005—2014年,1217栋建筑建造于2015—2017年.该方法同样适用于其他城市,为后续的建筑群能耗模拟提供了数据支持.

城市建筑集群能耗模拟(UBEM)与环境可持续导向的城市规划与设计:方法,工具和路径

本文从城市规划和设计的实际需求出发,首先简析了建筑能耗模拟在单体-街区/城区-城市等不同尺度应用的原理机制方面的差异;继而讨论了街区/城区尺度所对应的UBEM方法的最新进展;并通过对理想化街区的形态和功能布局对能耗影响的参数化模拟,探讨了使用UBEM工具辅助规划设计决策的潜力;最后对UBEM的应用场景进行了探讨,包括城市设计和管理辅助决策、节能城区评价基准建模、绿色生态城区性能设计标准优化等方面,以帮助合理引导节能低碳城区的理论研究和建设实践。

An integrated framework utilizing machine learning to accelerate the optimization of energy-efficient urban block forms

Urban block form significantly impacts energy and environmental performance.Therefore,optimizing urban block design in the early stages contributes to enhancing urban energy efficiency and environmental sustainability.However,widely used multi-objective optimization methods based on performance simulation face the challenges of high computational loads and low efficiency.This study introduces a framework using machine learning,especially the XGBoost model,to accelerate multi-objective optimization of energy-efficient urban block forms.A residential block in Nanjing serves as the case study.The framework commences with a parametric block form model driven by design variables,focusing on minimizing building energy consumption(EUI),maximizing photovoltaic energy generation(PVE)and outdoor sunlight hours(SH).Data generated through Latin Hypercube Sampling and performance simulations inform the model training.Through training and hyperparameter tuning,XGBoost’s predictive accuracy was validated against artificial neural network(ANN),support vector machine(SVM),and random forest(RF)models.Subsequently,XGBoost replaced traditional performance simulations,conducting multi-objective optimization via the NSGA-II algorithm.Results showcase the framework’s significant acceleration of the optimization process,improving computational efficiency by over 420 times and producing 185 Pareto optimal solutions with improved performance metrics.SHAP analysis highlighted shape factor(SF),building density(BD),and building orientation(BO)as key morphological parameters influencing EUI,PVE,and SH.This study presents an efficient approach to energy-efficient urban block design,contributing valuable insights for sustainable urban development.

城市尺度建筑节能规划的国际经验及启示

建筑能耗是城市能耗的主体。在城市规划层面进行大尺度建筑节能,能够在宏观层面有效把控城市尺度建筑能耗水平,降低建筑总体能耗;能够灵活协调现有城市空间环境建设管理与城市节能的关系,有效减少能耗花费,综合考虑各方面影响因素,制定科学有效的规划决策。本文对比了伦敦、纽约、东京、多伦多四个发达国家城市在城市尺度建筑节能规划方面的专项规划策略、规划政策管理、技术研究以及节能规划效果四方面经验,详细阐述了城市空间形态、城市微气候环境以及用能行为这三个影响城市尺度建筑节能规划的主要因素,并从规划框架建构、数据收集处理和跨学科技术支持三方面探讨了当前进行城市尺度建筑节能规划面临的困难与挑战,最终提出在宏观专项规划体系、社会监管、数据信息及跨领域合作等四方面的思考。