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Due to rapid urbanisation and the significant contribution of cities to worldwide energy use and greenhouse gas emissions, urban energy system planning is growing more important. Urban building energy modelling (UBEM) draws increasing attention in the energy modelling field due to its inherent capacities for modelling entire cities or building stocks, and the potential of varying data inputs, approaches and applications. This review aims to identify best practices and improvements for UBEM applications by examining previous research, with a focus on the currently least established approaches. Different archetype development procedures are analysed for common problems, six main under-developed input approaches or parameters are identified, and applications for future scenario development are surveyed. By analysing previous studies in related fields, this paper provides an overview of gaps in the published research and possible additions to future UBEM projects that can help expanding the existing modelling procedures. Comprehensive human behaviour models with additional aspects beyond occupant presence are identified as a major point of interest. Further research on socio-economic parameters, such as household income and demographics, are also suggested to further improve modelling. This study also underlines the potential for utilising UBEM as a tool for evaluating future climate change scenarios.

Building archetype identification is crucial for Urban Building Energy Modeling (UBEM), but is still considered one of the biggest challenges in this field. New methods of data acquisition, along with data mining techniques such as clustering, have recently received attention for the possibility of significantly increasing identification reliability and archetype accuracy. This paper aims to establish a new and simple clustering methodology for developing building archetypes for hybrid UBEM, using open data sets and multiple diverse variables, that is still reliable and possible to validate without the use of metered energy use or real building data. The methodology uses k-means clustering for 10 building parameters simultaneously, including socio-economic parameters obtained using spatial interpolation of statistical values. Building archetypes are successfully developed for the residential building stocks of two case study areas in Sweden. The results also show that the error metric values for multiple iterations diverge after a certain number of clusters, even when using the same clustering methodology on the same data set. This discovered effect, along with the combined use of one well-known and one novel error metric, constitutes a framework well adapted to accurately determining the optimal number of building archetypes.