Urban Building Energy Modelling: From Fragmented Efforts to a Common Foundation

Urban Building Energy Modelling (UBEM) is increasingly recognised as a key tool for bridging building-scale analysis and city-level planning, supporting the decarbonisation of urban areas. It enables the assessment of building performance and the exploration of retrofit strategies, policy scenarios, and renewable integration. Over the past decade, UBEM has continuously evolved, encompassing physics-based, data-driven, and hybrid approaches, often supported by archetype generation and GIS workflows. Recent research also integrates microclimate, mobility, and green infrastructure. While this enriches analysis outcomes, it also leads to fragmentation, limiting tools’ comparability, transparency, and practical use by policymakers. This perspective paper provides a critical interpretation of the recent evolution in UBEM and proposes a goal-oriented classification of modelling approaches based on their final objectives rather than on calculation methodology. The paper also identifies three key gaps: the lack of scalable and consistent data frameworks, the absence of a shared terminology, and the need for criteria to assess whether models are “fit for purpose.” Addressing these issues through improved data infrastructures, conceptual clarity, and reliability assessment can support convergence without limiting innovation, fostering more robust and decision-relevant UBEM applications to support the energy transition.

Authors

Institutions

Publication Details

Journal
Energies
Published
2026-07-16
DOI
https://doi.org/10.3390/en19143351
Primary Topic
Building Energy and Comfort Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Urban Building Energy Modelling: From Fragmented Efforts to a Common Foundation

Francesco Causone, Martina Ferrando
Energies
Building Energy and Comfort Optimization
article

Urban Building Energy Modelling: From Fragmented Efforts to a Common Foundation

Francesco Causone, Martina Ferrando
article en

Abstract

Urban Building Energy Modelling (UBEM) is increasingly recognised as a key tool for bridging building-scale analysis and city-level planning, supporting the decarbonisation of urban areas. It enables the assessment of building performance and the exploration of retrofit strategies, policy scenarios, and renewable integration. Over the past decade, UBEM has continuously evolved, encompassing physics-based, data-driven, and hybrid approaches, often supported by archetype generation and GIS workflows. Recent research also integrates microclimate, mobility, and green infrastructure. While this enriches analysis outcomes, it also leads to fragmentation, limiting tools’ comparability, transparency, and practical use by policymakers. This perspective paper provides a critical interpretation of the recent evolution in UBEM and proposes a goal-oriented classification of modelling approaches based on their final objectives rather than on calculation methodology. The paper also identifies three key gaps: the lack of scalable and consistent data frameworks, the absence of a shared terminology, and the need for criteria to assess whether models are “fit for purpose.” Addressing these issues through improved data infrastructures, conceptual clarity, and reliability assessment can support convergence without limiting innovation, fostering more robust and decision-relevant UBEM applications to support the energy transition.

EnergiesVol. 19(14)
Politecnico di Milano (IT)
Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Building Energy and Comfort Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.