Addressing uncertainty to achieve stability in urban building energy modeling: A comparative study of four possible approaches

Abstract

Urban building energy modeling (UBEM) empowers the construction of green and low-carbon cities. However, its development is hindered by the uncertainty of data inputs, including inherent uncertain data (IUD), measurement uncertain data (MUD) and scenario uncertain data (SUD). This paper employed one typical MUD, namely, the thermal parameters of construction assemblies, as the study object to analyze the accuracy and stability of UBEM using four different approaches, i.e., archetypes built with standards, archetypes built with local datasets, probabilistic models and urban factor methods. The results showed that when focusing solely on thermal parameters, the RE values could reach 500 % at the building level but tended to converge to less than 90 % at the district level. In addition, the mean of relative errors at the building level influenced the accuracy at the district level as well as the rate of mean convergence. However, this metric did not affect the threshold to attain range convergence, since its number was fixed, neither related to the sample size nor to the calculation accuracy. This study emphasized that using real data could enhance the accuracy of UBEM, regardless of the archetype or the stochastic approach used, but the distinctions mainly occurred at the building level. Moreover, the large-scale simulation work could be transformed into the task of calculating energy use data for several convergence units, each consisting of dozens of buildings, since these units were able to exhibit stability on their own.