Reducing uncertainty of building shape information in urban building energy modeling using Bayesian calibration

This study proposes urban building energy modeling that extends beyond single-building-level models to the urban level. However, most urban building energy models use representative buildings that may not accurately reflect the diversity of building shapes, systems, and envelope performance when conducting building energy evaluations at the urban scale. To address this issue, previous studies have utilized representative buildings and Bayesian calibration to estimate uncertain building information parameters without considering building shape information. Therefore, the primary objective of this study is to estimate building shape information using artificial neural networks and Bayesian calibration based on building energy consumption data to identify the shape information uncertainty of representative buildings. The results indicate that some shape information can be estimated by comparing the overall distribution of the building stock using the two-sample Kolmogorov–Smirnov test. Furthermore, we found that the proposed energy modeling methodology yields energy consumption patterns similar to those of the target building stock. This preliminary investigation addresses the uncertainty of representative buildings in urban-scale modeling, elucidates the relationship between building form and energy consumption, and introduces a method for inferring shape information from energy consumption data.