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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 被引量:10
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作者 杨峰 姜之点 《建筑科学》 CSCD 北大核心 2021年第8期17-24,共8页
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... 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。 展开更多
关键词 城市建筑集群能耗模拟 城市微气候 建筑集群形态 城市设计 评价工具
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自下而上的城市建筑群能耗模型研究综述
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作者 雷武强 张永益(指导) +4 位作者 李静薇 胡佳和 张曦兮 刘艳 吴春金 《建筑节能(中英文)》 CAS 2023年第9期108-113,共6页
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。 展开更多
关键词 城市建筑群能耗模型 自下而上 模拟工具 数据获取
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Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities:Current trends and future outlook 被引量:1
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作者 Nilabhra Mondal Prashant Anand +5 位作者 Ansar Khan Chirag Deb David Cheong Chandra Sekhar Dev Niyogi Mattheos Santamouris 《Building Simulation》 SCIE EI CSCD 2024年第5期695-722,共28页
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 urba... 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. 展开更多
关键词 urban heat island(UHI) urbanmicro-climate urban morphology urban building energy modelling(ubem) digitaltwin shorttermload forecasting(STLF) googleearthengine(GEE)
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Modelling occupant behaviour for urban scale simulation:Review of available approaches and tools 被引量:1
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作者 Aya Doma Mohamed Ouf 《Building Simulation》 SCIE EI CSCD 2023年第2期169-184,共16页
Urban building energy modelling(UBEM)is considered one of the high-performance computational tools that enable analyzing energy use and the corresponding emission of different building sectors at large scales.However,... Urban building energy modelling(UBEM)is considered one of the high-performance computational tools that enable analyzing energy use and the corresponding emission of different building sectors at large scales.However,the efficiency of these models relies on their capability to estimate more realistic building performance indicators at different temporal and spatial scales.The uncertainty of modelling occupants'behaviours(OB)aspects is one of the main reasons for the discrepancy between the UBEM predicted results and the building's actual performance.As a result,research efforts focused on improving the approaches to model OB at an urban scale considering different diversity factors.On the other hand,the impact of occupants in the current practice is still considered through fixed schedules and behaviours pattern.To bridge the gap between academic efforts and practice,the applicability of OB models to be integrated into the available UBEM tools needs to be analyzed.To this end,this paper aims to investigate the flexibility and extensibility of existing UBEM tools to model OB with different approaches by(1)reviewing UBEM's current workflow and the main characteristics of its inputs,(2)reviewing the existing OB models and identifying their main characteristics and level of details that can contribute to UBEM accuracy,(3)providing a breakdown of the occupant-related features in the commonly used tools.The results of this investigation are relevant to researchers and tool developers to identify areas for improvements,as well as urban energy modellers to understand the different approaches to model OB in available tools. 展开更多
关键词 ubem ubem tools occupant behaviour level of details
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The potential of remote sensing and GIS in urban building energy modelling
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作者 Arunim Anand Chirag Deb 《Energy and Built Environment》 EI 2024年第6期957-969,共13页
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 emiss... 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. 展开更多
关键词 Remote sensing GIS Urban Building Energy Modelling(ubem) UAV Building energy demand
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