Buildings.city
Scalable urban building energy modeling and carbon emissions mapping using open archetype templates
Datos Bibliográficos
| ID | 21478490 |
|---|---|
| Autores | Pengdi Lyu (National University of Singapore), Hongyao Wong (National University of Singapore), Royden Soh (National University of Singapore), Tao Wang (0000-0002-8367-8946, National University of Singapore), Yu Qian Ang (0000-0002-9757-606X, National University of Singapore, autor de correspondencia) |
| Año | 2026 |
| Volumen | 128 |
| Páginas | 102453 |
| Fecha de publicación | 2026-09-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Computers Environment and Urban Systems (JOURNAL) |
| Identificadores de la revista | ISSN: 0198-9715 • E-ISSN: 1873-7587 |
| Editorial | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.compenvurbsys.2026.102453 |
| OpenAlex | W7160902949 |
| Idioma | EN |
| Citas recibidas | 1 |
| Referencias citadas | 29 |
Cities and municipalities often struggle to scale building-level energy and carbon assessments to the urban scale, due to incomplete data, inconsistent input quality, and a lack of benchmarks. This study presents Buildings.city, an open-source Urban Building Energy Modeling (UBEM) framework and reproducible toolkit for city-scale carbon emissions accounting and mapping. The platform integrates globally available multi-source geospatial data, machine-learning-based archetype inference, and archetype-specific energy simulations (along with the associated templates) to generate detailed building-level carbon maps. To demonstrate the framework's adaptability to diverse urban contexts, the GitHub repository provides a baseline example of Zurich, and the entire toolkit was validated through a city-scale proof of concept in Singapore ( Buildings.sg ). In the full deployment, a predictive model achieved >75% accuracy in inferring missing building archetypes, addressing data gaps in OpenStreetMap (OSM). We also established a set of open-source building energy modeling packages and simulation templates for 23 archetypes, along with a web platform that visualizes simulated carbon emissions across approximately 120,000 buildings to support interactive analysis and policy decision-making. Ultimately, through a modular architecture that integrates diverse datasets, the Buildings.city toolkit provides a transparent, adaptable baseline for universal application. • Developed Buildings.city, a scalable open-source UBEM and carbon mapping toolkit. • Validated via Singapore's first national UBEM templates ( Buildings.sg ). • Machine learning infers missing building archetypes with >75% accuracy. • Jointly models operational and probabilistic embodied carbon for ∼120,000 buildings. • Modular architecture and open templates enable rapid transfer to other cities
Archetype · Carbon fibers · Efficient energy use · Greenhouse gas · Scalability · Template · Architecture and Computational Design · Building Energy and Comfort Optimization · Wind and Air Flow Studies · Architecture
| Obras citantes distintas | 1 |
|---|---|
| Citas por año | 1 |
| Intervalo de citas | 2026 - 2026 (1) |
| Velocidad de citación | current |
| Altamente citado | No |