Generating synthetic occupants for use in building performance simulation

Date Published
11/2021
Publication Type
Journal Article
Authors
DOI
10.1080/19401493.2021.2000029
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.

 
Journal
Journal of Building Performance Simulation
Volume
14
Year of Publication
2021
Issue
6
Pagination
712 - 729
ISSN Number
1940-1493
URL
Short Title
Journal of Building Performance Simulation
Keywords
Organizations
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