Urban building energy modeling from geo-referenced energy performance certificate data: Development, calibration, and validation

Johari, Fatemeh; Shadram, Farshid; Widén, Joakim

doi:10.1016/j.scs.2023.104664

Cited by 30 publications

(14 citation statements)

References 38 publications

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“…A meta-analysis on building LCA studies conducted by Bischof et al [76] concludes that most building stock modelling approaches evade the widespread bottom-up/top-down categorisation, rather showing characteristics of both categories. However, archetype assignment is the most common approach for data enrichment in UBEM [79,80]. In the context of this research, archetypes are statistical renditions of building properties that are assigned by a limited range of input variables, in particular building age classes and usage types.…”

Section: Current Focal Points In Researchmentioning

confidence: 99%

On the Potential of District-Scale Life Cycle Assessments of Buildings

et al. 2023

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Climate neutrality goals in the building sector require a large-scale estimation of environmental impacts for various stakeholders. Life Cycle Assessment (LCA) is a viable method for this purpose. However, its high granularity, and subsequent data requirements and effort, hinder its propagation, and potential employment of Machine Learning (ML) applications on a larger scale. The presented paper outlines the current state of research and practice on district-scale building LCA in terms of standards, software and certifications, and data availability. For this matter, the authors present the development and application of two district-scale LCA tools, Teco and DisteLCA, to determine the Global Warming Potential (GWP) of three different residential districts. Both tools employ data based on (including, but not limited to) CityGML, TABULA, and ÖKOBAUDAT. The results indicate that DisteLCA’s granular approach leads to an overestimation of environmental impacts, which can be derived from the statistical approach to operational energy use and related emissions. While both tools lead to substantial time savings, Teco requires less manual effort. The linkage of the aforementioned data sources has proven laborious and could be alleviated with a common data framework. Furthermore, large-scale data analysis could substantially increase the viability of the presented approach.

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Section: Current Focal Points In Researchmentioning

confidence: 99%

On the Potential of District-Scale Life Cycle Assessments of Buildings

et al. 2023

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“…Following the methodology suggested in a previous study conducted by the authors [23], the UBEM is developed using national open data for residential buildings in Sweden. These data are divided into two parts, namely GIS data and energy performance certificate (EPC) data.…”

Section: Building Datamentioning

confidence: 99%

Analysis of Large-Scale Energy Retrofit of Residential Buildings and Their Impact on the Electricity Grid Using a Validated Ubem

Johari,

Lindberg,

Ramadhani

et al. 2023

Preprint

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“…Deng et al [22] developed AutoBPS and simulated six types of buildings in Changsha city using the building-by-building method, and then conducted three energy saving measures. Johari et al [23] observed a downward trend in the MAPE from building to district and city levels with single-family and multi-family buildings in different vintages. They considered the shading in over 12,150 buildings in the case study city of Borlänge when the building was in a dense urban area.…”

Section: Introductionmentioning

confidence: 99%

Urban Building Energy Modeling with Parameterized Geometry and Detailed Thermal Zones for Complex Building Types

Xi,

Zhang,

Ren

et al. 2023

Buildings

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Urban building energy modeling (UBEM) has attracted wide attention to the requirement for global carbon emission reduction. This paper presents a UBEM tool, AutoBPS-Param, to generate building energy models (BEMs) with parameterized geometry and detailed thermal zones, especially for complex building types, considering the shading effect from surrounding buildings simultaneously. Three building number scales and four scenarios were analyzed in the hotel-related buildings in Changsha, China. For the prototype modeling of Scenario 1, eighteen prototype building energy models for six building types in three vintages were created, and their simulation results were aggregated based on their representative floor areas. For AutoBPS-Param of Scenario 4, the method created one EnergyPlus (Version: 9.3.0) model for each building. The geometry of the prototype model was scaled and modified based on the target building’s length, width, and number of stories. The surrounding buildings were also added to the AutoBPS-Param simulation to better capture the urban dynamic impact. The results showed that the annual electricity and natural gas energy use intensity (EUI) of the pre-2005 HotelOffice prototype model was 172.25 and 140.45 kWh/m2. In contrast, with the AutoBPS-Param method, the annual electricity EUIs of 71 HotelOffice buildings constructed before 2005 ranged from 159.51 to 213.58 kWh/m2 with an average of 173.14 kWh/m2, and the annual gas EUIs ranged from 68.02 to 229.12 kWh/m2 with an average of 108.89 kWh/m2. The proposed method can better capture the diversity of urban building energy consumption.

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Urban building energy modeling from geo-referenced energy performance certificate data: Development, calibration, and validation

Cited by 30 publications

References 38 publications

On the Potential of District-Scale Life Cycle Assessments of Buildings

On the Potential of District-Scale Life Cycle Assessments of Buildings

Analysis of Large-Scale Energy Retrofit of Residential Buildings and Their Impact on the Electricity Grid Using a Validated Ubem

Urban Building Energy Modeling with Parameterized Geometry and Detailed Thermal Zones for Complex Building Types

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