From static archetypes toward dynamic, condition-aware archetypes in UBEM: an integrative review with an illustrative case demonstration

Urban Building Energy Modeling (UBEM) is often used for large-scale renovation planning. However, UBEM traditionally uses static archetypes, which fail to reflect the current state of buildings, particularly in partially renovated stocks. This integrative review examines the limitations of static archetype-based UBEM and the role of GIS, reality capture, and AI in providing condition-aware inputs, supported by a problem demonstration case in Tallinn, Estonia. Up-to-date information about three important condition attributes: the presence of side-wall windows, the insulation state of the building envelope, and the level of window replacement, can impact heating demand by up to 60%. Static archetype-based models were found to overestimate energy use by 14%–60% because they ignore such variations. The literature review identifies key research challenges in integrating digital data sources into UBEM, and the demonstration case provides practical evidence motivating the proposed conceptual Dynamic Archetype framework, which could update UBEM inputs with observed current building conditions and, in future applications, support more accurate and scalable renovation planning.