Urban-Building Energy Modelling (UBEM) tools play a crucial role in analysing and optimizing energy use within cities. Among the available approaches, the bottom-up physics-based one is the most versatile for urban development and management applications. However, their accuracy is often limited by the inability to capture the dynamic impact of occupants’ presence and actions (i.e., Occupant Behaviour) on building energy use patterns. While recent research has explored advanced Occupant Behaviour (OB) modelling techniques that incorporate stochasticity and contextual influences, current UBEM practices primarily rely on static occupant profiles, due to limitations in the software itself. This paper addresses this topic by conducting a thorough literature review to examine existing OB modelling techniques, data sources, key features and detailed information that could enhance UBEM simulations. Furthermore, the flexibility of available UBEM tools for integrating advanced OB models will be assessed, along with the identification of areas for improvement. The findings of this review are intended to guide researchers and tool developers towards creating more robust and occupant-centric urban energy simulations.

Urban Building Energy Modelling (UBEM) represents a comprehensive approach to investigate the intricate interplay of the various factors impacting energy use of groups of buildings, offering invaluable insights for urban planners, architects, building engineers, and policymakers. Nonetheless, available UBEM tools are still “research tools” and lack a unified standard addressing input, output, nomenclature, and calculation approaches. In this context, this study aims to conduct a comprehensive comparative analysis of two of the most used UBEM tools: Integrated Computational Design (iCD), the commercial tool provided by the Integrated Environmental Solutions (IES) company, and Urban Modelling Interface (umi), developed by the Massachusetts Institute of Technology (MIT). The comparative analysis includes each step of the UBEM workflow: the creation of the model, the assignment of input data, energy simulation, and visualisation and exportation of results. The tools are tested through the simulation of a case study to provide insights on the rationale and informed use of the tools, highlighting the risks associated with use by modellers with different levels of expertise. Moreover, this study provides tool developers and the scientific community with suggestions for major areas of improvement and standardisation in the field of UBEM, since substantial differences are still reported with respect to output, input, nomenclature, and calculation approaches.

Urban Building Energy Modeling (UBEM) plays a critical role in supporting data-driven strategies for the energy transition of cities. However, its application is often hindered by the lack of harmonized, high-quality input data representing the building stock. This paper presents the methodology and outputs of a national research project to construct an Italian library of reference buildings suitable for UBEM applications described with scorecards. The methodological workflow included six key phases: definition of a national data classification framework, acquisition and integration of heterogeneous data sources, data harmonization, statistical analysis and clustering, archetype formalization, and dissemination. The result is a library of 380 scorecards covering residential, educational, office, commercial, and catering buildings across multiple climate zones and construction periods. Each scorecard is based on empirical data from public databases, field surveys, or technical standards, and includes detailed descriptions of geometry, envelope characteristics, HVAC systems, internal gains, and ventilation. The scorecards are shared openly on the project’s website and were built to work with different UBEM platforms. Overall, both the method and the results help bring more consistency to UBEM practice and support better, data-driven urban energy planning.