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Urban building energy modeling (UBEM): a systematic review of challenges and opportunities

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Abstract

In recent decades, urban energy consumption and carbon emissions have expanded rapidly on a global scale. Building sector, in particular, accounts for approximately 40% of overall energy use. Urban planners and decision-makers have a significant responsibility to achieve sustainable energy and climate objectives. Urban building energy modeling (UBEM) has increased in popularity in recent years as a tool for calculating urban-scale energy use in buildings with limited resources, and that facilitated the formulation of new energy policies. However, published studies of UBEM methodologies and tools lack comprehensive examinations of the potential limitations of research and the prospects of future opportunities. This paper provides a complete conceptual framework for UBEM based on extensive literature reviews and prior researchers’ work. In addition to providing a comprehensive understanding of the various UBEM approaches and tools, future research directions are explored. The results demonstrate that earlier researches did not adequately account for input uncertainty and lacked proper simulation and calibration control for algorithms/models. These challenges not only increased the workload and computational burden of modelers but also diminished the precision of model calculations. In response, this paper provides targeted recommendations for each essential phase of the present UBEM workflow, namely model input, model development, and model calibration, to address these limitations, as well as a comprehensive analysis of future prospects. The main aim of the research is to further UBEM development as a faster, more accurate and multiscale supportive tool and establish a framework for future UBEM methods.

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Abbreviations

ABM:

Agent Based Modeling

AI:

Artificial Intelligence

AMY:

Actual Meteorological Year

ANN:

Artificial Neural Network

API:

Application Programming Interface

BEM:

Building Energy Modeling

CityBES:

City Building Energy Saver

CityGML:

City Geography Markup Language

COP:

Coefficient of Performance

CVRMSE:

Coefficient of the Variation of the Root Mean Square Error

DHW:

Domestic Hot Water

EPC:

Environmental Performance Certificate

EUI:

Energy Use Intensity

EPFL:

École Polytechnique Fédérale de Lausanne

GDP:

Gross Domestic Product

GHG:

Green House Gas

GIS:

Geographic Information System

HVAC:

Heating, Ventilation and Air Conditioning

ICT:

Information and Communications Technology

IoT:

Internet of Things

LBNL:

Lawrence Berkeley National Laboratory

LOD:

Level of Detail

MAE:

Mean Absolute Error

MVU:

Minimum Viable Urban Building Energy Modeling

ML:

Machine Learning

OSM:

Open Street Map

ORNL:

Oak Ridge National Laboratory

RMSE:

Root Mean Squared Error

TMY:

Typical Meteorological Year

UBEM:

Urban Building Energy Modeling

UCM:

Urban Climate Modeling

UHI:

Urban Heat Island effect

UMI:

Urban Modeling Interface

WWR:

Window-Wall Ratio

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Acknowledgements

This research was supported under the project of Research and Demonstration of Key Technologies for Low-Carbon Design and Optimization of Community (Park) Regional Integrated Energy Systems, by the Ningbo Science and Technology Bureau Major Science and Technology Programme, with project code 2022Z161. We would also like to express our sincere gratitude to Ningbo Energy Group Co., Ltd. and Ningbo Yongneng Integrated Energy Services Co., Ltd. for their strong support of the research.

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Authors and Affiliations

  1. Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China

    Dezhou Kong, Zhiang Zhang & Tingyue Gu

  2. School of Architecture, Qingdao City University, Qingdao, China

    Ali Cheshmehzangi

  3. Network for Education and Research for Peace and Sustainability (NERPS), Hiroshima University, Hiroshima, Japan

    Ali Cheshmehzangi

  4. School of Computer Science, University of Lincoln, Lincoln, UK

    Saeid Pourroostaei Ardakani

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Contributions

D.K.: conceptualization, methodology, writing - original draft and writing - review & editing. A.C.: supervision, project administration, funding acquisition. Z.Z.: methodology and writing - review & editing. S.A.: investigation, resources and software. T.G.: formal analysis, resources and writing - review & editing.

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Kong, D., Cheshmehzangi, A., Zhang, Z. et al. Urban building energy modeling (UBEM): a systematic review of challenges and opportunities. Energy Efficiency 16, 69 (2023). https://doi.org/10.1007/s12053-023-10147-z

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