Generating synthetic occupants for use in building performance simulation. Issue 6 (2nd November 2021)

• Record Type: Journal Article
• Authors: Putra, Handi Chandra; Andrews, Clinton; Hong, Tianzhen
• Is Part Of: Journal of building performance simulation. Volume 14:Issue 6(2021)
• Page Start: 712
• Page End: 729
• Publication Date: 2021-11-02
• Subjects: Synthetic occupants -- occupant behaviour -- building performance simulation -- occupant behaviour model -- data model; 690.0113
• DOI: 10.1080/19401493.2021.2000029
• Languages: English
• ISSNs: 1940-1493

Abstract

Abstract : Occupant behaviour simulation frameworks can employ synthetic populations to characterize occupancy and behavioural patterns in buildings based on observed demographic data at a certain geographical location. For buildings, very few synthetic occupant populations have been generated. This paper uses a Bayesian Networks (BN) structural learning approach to synthesize populations of occupants in a multi-family housing case study. Two additional cases of office occupants and senior housing residents are considered as a cross-case comparison. We draw upon the extended version of drivers-needs-actions-systems (DNAS) framework to guide the selection of variables and data imputation. Our results show that the BN approach is powerful in learning the structure of data sets. The synthetic data sets successfully match the joint distributions of the underlying combined data sets. Experiments on the multi-family housing particularly show better performance than the office and senior housing cases.