Urban Building Energy Modeling (UBEM) Tools: A State-of-the-Art Review of bottom-up physics-based approaches
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Abstract (arXiv)
Regulations corroborate the importance of retrofitting existing building stocks or constructing new energy efficient district. There is, thus, a need for modeling tools to evaluate energy scenarios to better manage and design cities, and numerous methodologies and tools have been developed. Among them, Urban Building Energy Modeling (UBEM) tools allow the energy simulation of buildings at large scales. Choosing an appropriate UBEM tool, balancing the level of complexity, accuracy, usability, and computing needs, remains a challenge for users. The review focuses on the main bottom-up physics-based UBEM tools, comparing them from a user-oriented perspective. Five categories are used: (i) the required inputs, (ii) the reported outputs, (iii) the exploited workflow, (iv) the applicability of each tool, and (v) the potential users. Moreover, a critical discussion is proposed focusing on interests and trends in research and development. The results highlighted major differences between UBEM tools that must be considered to choose the proper one for an application. Barriers of adoption of UBEM tools include the needs of a standardized ontology, a common three dimensional city model, a standard procedure to collect data, and a standard set of test cases. This feeds into future development of UBEM tools to support cities' sustainability goals.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Feb 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2.
3 papers.
- 2021Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Networkcited by 5
- 2024Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modelingcited by 1
- 2024Adopting Explainable-AI to investigate the impact of urban morphology design on energy and environmental performance in dry-arid climatescited by 0
- 2024Adopting Explainable-AI to investigate the impact of urban morphology design on energy and environmental performance in dry-arid climates13 Dec 2024
- 2024Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling11 Apr 2024
- 2021Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network27 May 2021