Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis

Ali, Usman; Shamsi, Mohammad Haris; Hoare, Cathal; Mangina, Eleni; O’Donnell, James

doi:10.1016/j.enbuild.2021.111073

Cited by 137 publications

(55 citation statements)

References 91 publications

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Section: Ubem and Bemmentioning

confidence: 99%

“…In BEM, the modelling techniques mainly comprise of black-box models (empirical or statistical-based), and grey-box models (both physics and statistical-based) and white-box models (forward modelling or physics-based) [17][18][19]. For UBEM, two more categories are defined, namely, top-down and bottom-up [9,14,19]. The top-down approach includes econometric and technological models [20] by employing the statistical information and macroeconomic variables for determining energy demand projections [19].…”

Section: Ubem and Bemmentioning

confidence: 99%

“…For UBEM, two more categories are defined, namely, top-down and bottom-up [9,14,19]. The top-down approach includes econometric and technological models [20] by employing the statistical information and macroeconomic variables for determining energy demand projections [19]. It is suitable for an aggregated and broad level large-scale analysis as it couples variables such as gross domestic product, fuel costs, and income to the energy sector [20,21].…”

Section: Ubem and Bemmentioning

confidence: 99%

“…In many of the bottom-up UBEM-based physical approaches, enrichment of pure geometrical models is considered an important step [12,19]. For an efficient simulation workflow, parameters such as heating and cooling schedules, and material properties, etc., are enriched directly in the input data model.…”

Section: Archetype-based Approachmentioning

confidence: 99%

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Open-Source Tool for Transforming CityGML Levels of Detail

et al. 2021

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Urban Building Energy Modelling (UBEM) requires adequate geometrical information to represent buildings in a 3D digital form. However, open data models usually lack essential information, such as building geometries, due to a lower granularity in available data. For heating demand simulations, this scarcity impacts the energy predictions and, thereby, questioning existing simulation workflows. In this paper, the authors present an open-source CityGML LoD Transformation (CityLDT) tool for upscaling or downscaling geometries of 3D spatial CityGML building models. With the current support of LoD0–2, this paper presents the adapted methodology and developed algorithms for transformations. Using the presented tool, the authors transform open CityGML datasets and conduct heating demand simulations in Modelica to validate the geometric processing of transformed building models.

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Section: Ubem and Bemmentioning

confidence: 99%

Section: Ubem and Bemmentioning

confidence: 99%

Section: Ubem and Bemmentioning

confidence: 99%

Section: Archetype-based Approachmentioning

confidence: 99%

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Open-Source Tool for Transforming CityGML Levels of Detail

et al. 2021

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“…In the recent years, there has been a growing number of studies and investigations dealing with energy modelling, analysis and performance optimisation of urban areas, districts, and cities (Ali, et al, 2021). The complexity of building cluster simulations is addressed by Eicker in "Urban energy systems for low carbon cities'' and it is assessed that the most accurate cluster models need many inputs and often the predictions are not as accurate as single building models, as it is hard to schedule human behaviour, urban climate, and characterize the complexity of a single building on a larger basis.…”

Section: Introductionmentioning

confidence: 99%

Design, Modelling and Performance Evaluation of a Positive Energy District in a Danish Island

Jepsen

Haut

Jradi

2022

Future Cities and Environment

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Nowadays, cities and districts are becoming more and more interconnected in terms of energy supply systems, and the transition towards smart connected thermal and electrical grids is inevitable. While single building-level modelling has demonstrated numerous technical and economic benefits, it is vital to scale up the approach and consider the larger picture of cluster of buildings, districts, and cities. The island of AErø in Denmark has an ambitious goal to become the first CO 2 neutral and renewable energy self-sufficient Danish Island by 2025, as well as becoming a fossil-fuel free island by 2030. With the ambitious energy and environmental aims of AErø island, this work aims to investigate the capability of establishing part of the island as the first Danish positive energy district (PED) and assess various design scenarios and possibilities of upgrading/modifying the current scheme and composition of energy supply and distribution. An open-source urban scale modelling tool is used, City Energy Analyst (CEA), where the district is modelled considering all specifications and characteristics of buildings and the corresponding energy supply systems. The base case scenario is simulated, and the performance is calibrated using actual data. Then, multiple energy improvement measures targeting the buildings envelope as well as the energy generation and supply systems are developed and simulated to assess the impact on the overall energy consumption for heating and electricity. The results show that a PED can be established employing an improvement package of envelope-targeting measures, energy systems upgrades and renewable energy systems expansion.

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Occupant‐Driven Urban Building Energy Efficiency via Ambient Intelligence

Abbasabadi,

Ashayeri

2024

Artificial Intelligence in Performance‐Driven Design

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Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis

Cited by 137 publications

References 91 publications

Open-Source Tool for Transforming CityGML Levels of Detail

Open-Source Tool for Transforming CityGML Levels of Detail

Design, Modelling and Performance Evaluation of a Positive Energy District in a Danish Island

Occupant‐Driven Urban Building Energy Efficiency via Ambient Intelligence

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