https://doi. org/10.1016/j. enbuild. 2025.115843Volume 343,15 September 2025(1) Archetypes-based calibration for urban building energy modelling by Moa Mattsson,Itai Danielski,Thomas Olofsson,et al,Abstract:Reducing e...https://doi. org/10.1016/j. enbuild. 2025.115843Volume 343,15 September 2025(1) Archetypes-based calibration for urban building energy modelling by Moa Mattsson,Itai Danielski,Thomas Olofsson,et al,Abstract:Reducing energy use w ithin the building sector is vital to create sustainable cities and mitigate global w arming. Urban building energy modelling (UBEM) is useful to evaluate energy demand and renovation potential in districts.展开更多
While the implementation of sustainable urban planning has proven to be one of the primary goals to reduce the climate change impact,the rapid adoption of nearly Zero Energy Buildings(nZEB)concept in the building sect...While the implementation of sustainable urban planning has proven to be one of the primary goals to reduce the climate change impact,the rapid adoption of nearly Zero Energy Buildings(nZEB)concept in the building sector is inevitable to reach that objective.Following this trend,this article focuses on the implementation of a parametric digital workflow to evaluate the energy performance of a nearly zero energy high-rise 23-storey office building in the climatic and urban contexts of Casablanca at the early design stage.In the scope of this study,Grasshopper-based digital workflow permits to investigate the impact of 147 parametric building designs,which are generated by varying the building’s shape factor and orientation on thermal cooling and heating demand and global solar energy production.The outcomes of this holistic methodology highlight the design trade-offs between energy efficiency strategies and energy performance of building-integrated photovoltaic(BIPV)and photovoltaic(PV)systems,aiming to reach optimized nZEB.Moreover,the results of the study suggest that it is possible to reach an annual load match equivalent to 29.68%.The findings also underscore the significant role of the BIPV systems in shifting towards the goal of net zero energy,accounting for up to 64.43%of the total solar energy output and contributing in total up to 17.62%to the yearly self-sufficiency.In addition,the energy balance evaluation,when assessed on an hourly basis,reveals that the BIPV system significantly improves the daily load cover factor,achieving a value of 12.45%,and increases up to 20.62%when considering also the rooftop PV,particularly during spring season.Finally,the capacity credit factor is improved by up to 31.27%,which is a significant share of grid connection reduction compared to the same building relying totally on the grid for its energy needs.展开更多
Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch...Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch images from Google Street View(GSV);(2)label images with a custom Rhino-based tool to aid annotation of occluded glazing;(3)detect wall,garage,and glazing objects by training and validating a YOLOv9 deep learning model with three added post-scripts;(4)calculate WWR at façade,building,and district scales;and(5)simulate district energy consumption in an urban building energy model(UBEM).Results include a 96%image-capture rate from GSV,indicating a robust extraction and stitching algorithm.Converting model detections into WWR,94%and 100%of façades have detected WWRs within±5%and±10%of ground truth WWRs,respectively.A novel automatic algorithm upscales façade detection to estimate WWR at non-street-facing sides and rears,resulting in distinct WWRs for each face of each building.For a case study in Turin,Italy,WWR detections are+5.2%and+6.9%greater when upscaling based on OpenStreetMap and municipal GIS data,respectively,compared to TABULA,leading to 1.5%and 35.5%increases in heating and cooling energy need in the UBEM.The workflow is made openly available to support future research in other contexts.展开更多
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
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
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
文摘https://doi. org/10.1016/j. enbuild. 2025.115843Volume 343,15 September 2025(1) Archetypes-based calibration for urban building energy modelling by Moa Mattsson,Itai Danielski,Thomas Olofsson,et al,Abstract:Reducing energy use w ithin the building sector is vital to create sustainable cities and mitigate global w arming. Urban building energy modelling (UBEM) is useful to evaluate energy demand and renovation potential in districts.
文摘While the implementation of sustainable urban planning has proven to be one of the primary goals to reduce the climate change impact,the rapid adoption of nearly Zero Energy Buildings(nZEB)concept in the building sector is inevitable to reach that objective.Following this trend,this article focuses on the implementation of a parametric digital workflow to evaluate the energy performance of a nearly zero energy high-rise 23-storey office building in the climatic and urban contexts of Casablanca at the early design stage.In the scope of this study,Grasshopper-based digital workflow permits to investigate the impact of 147 parametric building designs,which are generated by varying the building’s shape factor and orientation on thermal cooling and heating demand and global solar energy production.The outcomes of this holistic methodology highlight the design trade-offs between energy efficiency strategies and energy performance of building-integrated photovoltaic(BIPV)and photovoltaic(PV)systems,aiming to reach optimized nZEB.Moreover,the results of the study suggest that it is possible to reach an annual load match equivalent to 29.68%.The findings also underscore the significant role of the BIPV systems in shifting towards the goal of net zero energy,accounting for up to 64.43%of the total solar energy output and contributing in total up to 17.62%to the yearly self-sufficiency.In addition,the energy balance evaluation,when assessed on an hourly basis,reveals that the BIPV system significantly improves the daily load cover factor,achieving a value of 12.45%,and increases up to 20.62%when considering also the rooftop PV,particularly during spring season.Finally,the capacity credit factor is improved by up to 31.27%,which is a significant share of grid connection reduction compared to the same building relying totally on the grid for its energy needs.
文摘Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch images from Google Street View(GSV);(2)label images with a custom Rhino-based tool to aid annotation of occluded glazing;(3)detect wall,garage,and glazing objects by training and validating a YOLOv9 deep learning model with three added post-scripts;(4)calculate WWR at façade,building,and district scales;and(5)simulate district energy consumption in an urban building energy model(UBEM).Results include a 96%image-capture rate from GSV,indicating a robust extraction and stitching algorithm.Converting model detections into WWR,94%and 100%of façades have detected WWRs within±5%and±10%of ground truth WWRs,respectively.A novel automatic algorithm upscales façade detection to estimate WWR at non-street-facing sides and rears,resulting in distinct WWRs for each face of each building.For a case study in Turin,Italy,WWR detections are+5.2%and+6.9%greater when upscaling based on OpenStreetMap and municipal GIS data,respectively,compared to TABULA,leading to 1.5%and 35.5%increases in heating and cooling energy need in the UBEM.The workflow is made openly available to support future research in other contexts.
基金supported by the Fonds de Recherche du Québec Nature et technologies (FRQNT)Research Support for New Academics (Grant#315109)the Natural Sciences and Engineering Research Council of Canada (NSERC)Discovery Grant (RGPIN-2020-06804).
文摘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.
基金the Sponsored Research and Industrial Consultancy(SRIC)grant No:IIT/SRIC/AR/MWS/2021-2022/057the SERB grant No.IPA/2021/000081.
文摘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.
文摘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.
