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Urban Building Energy Modeling (UBEM) Tools: A State-of-the-Art Review of bottom-up physics-based approaches
Martina Ferrando, Francesco Causone, Tianzhen Hong, Yixing Chen
TL;DR
Cities need tools to evaluate building-energy scenarios, yet users must choose among UBEM tools while balancing complexity, accuracy, usability, and computing needs. This paper reviews and compares bottom-up physics-based UBEM tools from a user-oriented perspective across five categories, finding major differences that matter for selecting a tool and identifying adoption barriers including data, ontology, city-model, and testing standardization.
Problem
Choosing an appropriate UBEM tool remains challenging because users must balance model complexity, accuracy, usability, data quality, and computational effort.
Method
The paper conducts a user-oriented review comparing bottom-up physics-based UBEM tools across inputs, outputs, workflow, applicability, and potential users.
Results
The review identifies major differences among UBEM tools that must be considered when choosing one for a specific application.
Takeaways & Limitations
The findings inform tool selection and identify future opportunities and improvements for wider UBEM adoption.
Takeaways & Limitations
UBEM adoption and validation are constrained by mixed databases, dissimilar nomenclature, insufficient data, and the lack of standardized data-collection procedures.
Abstract
from arXiv · showhide
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.
1. Introduction
Urban building energy modeling supports city energy management and design, but selecting among bottom-up physics-based tools requires balancing detail, accuracy, usability, data quality, and computational effort. This review addresses the missing user-oriented comparison by classifying and comparing tools across five user-relevant categories.
- Buildings are major contributors to urban energy and material use, motivating better management of city energy and design.
- UBEM tools provide building-stock energy demand for benchmarking, scenario evaluation, and peak-load analysis at urban scales.
- The review fills a missing user-oriented overview by comparing tools across inputs, outputs, workflow, applicability, and potential users.
- Choosing a UBEM tool requires compromising among representation detail, model accuracy, usability, data quality, and computational effort.
- Bottom-up physics-based UBEM offers higher spatiotemporal detail and supports urban energy-system design and urban-development planning.
2. Bottom-up physics-based UBEM tools
The review surveys bottom-up physics-based UBEM tools developed for urban applications and compares their characteristics, workflows, and modeling capabilities. The selected tools differ in simulation engines, interfaces, inputs, modules, and intended applications.
- The review includes bottom-up physics-based tools created expressly for urban applications and used across different case studies.These tools model building stocks in detail using physics-based approaches.
- CitySim supports sustainable urban-settlement planning and simulations ranging from a few buildings to tens of thousands.Its thermal model uses an equivalent electrical circuit and can represent building subspaces linked through wall conductance.
- SimStadt supports urban energy-transition planning by rapidly creating scenarios using refurbishment rates, time horizons, and priority indexes.It is integrated with CityGML and Energy ADE three-dimensional city-model formats.
- The selected tools use varied architectures, including simulation platforms, Modelica libraries, databases with modules, and GUI-centered packages.Examples include COFFEE with BCL, OpenIDEAS with occupancy and building libraries, and six-module or seven-database structures.
3. Tools comparison
The comparison evaluates eight bottom-up physics-based UBEM tools across inputs, outputs, workflows, applicability, and potential users. Major differences include geometry and archetype handling, output capabilities, workflow coverage, computational scale, project type, and user expertise.
- The review compares eight tools using five categories: inputs, outputs, workflow, applicability, and potential users.The comparison was conducted at the beginning of 2020.
- 3.1. Input: Inputs generally combine weather data, building geometry, and thermophysical properties assigned to geometry entities.Additional inputs may include utility rates, energy conservation measures, and performance targets for specific analyses.
- 3.1. Input: UBEM tools simplify geometry, commonly using GIS-compatible building representations and archetypes to reduce computational time across many buildings.Archetypes represent prototype buildings with fabric, systems, and usage schedules; deterministic occupancy schedules are widespread, while stochastic modeling is available in StROBe and under study elsewhere.
- 3.2. Output: Outputs cover building energy use, resource potential, urban energy systems, and large-scale evaluations such as energy conservation measures and greenhouse-gas emissions.Building-related outputs commonly include heating and cooling thermal energy, domestic hot water demand, electric use, and sometimes daylight; some tools also support system optimization and resource-potential assessment.
- 3.3. Workflow: Workflows typically proceed through inputs, model, simulation, outputs, and post-processing, with tools differing in how much data management they perform themselves.Some tools provide graphical interfaces for input management, while others rely more heavily on external software or supporting methodologies.
- 3.4. Applicability and 3.5. Potential Users: Tool applicability depends on scale, computing platform, simulation engine, GIS integration, and project type, while users generally need BEM expertise and sometimes GIS, Python, Modelica, or urban-systems knowledge.Reduced-order models support large simulations on personal computers, whereas some multi-zone tools use web servers or high-performance computing; umi is suited to medium-scale analysis and new settlements.
4. Research and development potentials
UBEM research is advancing datasets, occupant and mobility modeling, urban climate integration, energy-system coupling, calibration, and life-cycle assessment, while computational and data constraints remain central.
- Research priorities: UBEM tools are evolving rapidly across building datasets, occupant behavior, mobility, urban microclimate, energy systems, outdoor comfort, and life-cycle assessment.The review identifies these topics as the main current research and development challenges.
- People movements and actions: Archetype-based modeling can produce non-realistic demand patterns because buildings with the same intended use are not differentiated.Stochastic occupant models are being explored to represent behavioral variation, but advanced models still require substantial data.
- People movements and actions: Only CitySim through MATsim-T and umi directly integrate models of people’s movement, while full coupling with comprehensive mobility models remains incomplete.umi provides simplified walking and cycling accessibility analyses based on amenity distance.
- Urban climate and infrastructure: UWG can generate hourly urban weather files from rural weather data, but most UBEM tools lack direct integration with detailed green-blue infrastructure modeling and outdoor comfort assessment.umi is directly integrated with UWG; other tools can use previously created UWG-like weather datasets.
- Calibration and validation: UBEM calibration is constrained by computational effort, data availability, and data granularity, while Bayesian calibration can require several thousands of simulations per building.These constraints make methods developed for conventional building energy modeling inappropriate for many UBEM applications.
- Life-cycle assessment: Detailed life-cycle assessment methodologies are difficult to integrate into UBEM tools because urban applications involve many buildings and building typologies.Some tools visualize equivalent CO2 emissions, but the framework settings of these analyses are not clear.
5. Conclusions and future outlook
The review compares bottom-up physics-based UBEM tools across five user-oriented feature categories and identifies challenges affecting their adoption. Its findings guide tool selection while pointing to standardization and data improvements for future development.
- The review compares UBEM tools by required inputs, reported outputs, workflow, applicability, and potential users.
- Bottom-up physics-based UBEM tools can assess building energy demand at high spatio-temporal resolution through numerical representations of building–environment interconnections.
- A common testbed cannot readily support all reviewed tools because their purposes and approaches differ substantially.
- Mixed databases, inconsistent nomenclature, insufficient data, and weak data-collection structures hinder model creation, tool comparison, and validation.The review proposes common city models, standardized datasets and nomenclature, structured data collection, and integration of measured data.
- The findings inform users and stakeholders selecting tools and identify future opportunities for developers and researchers seeking wider UBEM adoption.