The advantages and challenges of integrating archetypal building modelling for urban building energy models: A systematic review

With the acceleration of global urbanisation, energy consumption and carbon emissions from the building sector continue to rise, necessitating the optimisation of energy efficiency through large-scale Urban Building Energy Models (UBEMs). Archetypal building modelling (ABM), as a method that abstracts diverse buildings into representative archetypes, still faces challenges in standardising building types, modelling dynamic features, and ensuring cross-regional applicability. These limitations hinder UBEM's ability to balance accuracy and scalability in complex and heterogeneous urban building contexts. Based on the PRISMA methodology, this study systematically reviews relevant literature, outlining the definition and development trajectory of ABM, and analysing the impact of integrating static and dynamic datasets on model accuracy. Furthermore, the study explores the extraction of classification factors, clustering methods, and model validation strategies, while evaluating the influence of digital technologies on ABM. The findings indicate that although ABM effectively reduces modelling complexity and improves computational efficiency, it still encounters challenges such as limited classification criteria, insufficient data, and inadequate regional validation. Future efforts should focus on: firstly, enhancing the standardisation of building archetypes by establishing unified, multi-scale classification criteria; secondly, improving data collection and integration methods to support full lifecycle data for both new and existing buildings; thirdly, developing cross-scale validation frameworks to ensure accurate translation between macro-level predictions and micro-level assessments; and fourthly, leveraging digital twin technology and deep learning to achieve dynamic optimisation and real-time energy efficiency management of archetype models.