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。

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。

Modelling occupant behaviour for urban scale simulation:Review of available approaches and tools

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.

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.