An ontology to represent synthetic building occupant characteristics and behavior

Putra, Handi Chandra; Hong, Tianzhen; Andrews, Clinton

doi:10.1016/j.autcon.2021.103621

An ontology to represent synthetic building occupant characteristics and behavior

Journal Article · Sat Feb 13 00:00:00 EST 2021 · Automation in Construction

DOI:https://doi.org/10.1016/j.autcon.2021.103621· OSTI ID:1784298

Putra, Handi Chandra ^[1]; Hong, Tianzhen ^[1]; Andrews, Clinton ^[2]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Rutgers Univ., New Brunswick, NJ (United States). Rutgers Center for Green Building

Since the introduction of the occupant behavior Drivers-Needs-Actions-Systems (DNAS) framework in 2013, researchers have used the framework or further developed it based on their case studies, which include efforts to collect new data on occupant behaviors. The effort is often costly for the relatively few new data points added. Problems emerge when the already collected data do not meet the modelers' interoperability requirements. Previous studies addressed this issue by developing more sophisticated ontologies that enable integration with other datasets and synthetic data methodologies that would meet unique research applications. This paper presents an extension of the DNAS framework for the representation of synthetic occupant data to support various applications and use cases across the building life cycle. An agent-based modeling application is one of our motivations that requires more elaborate characteristics of an occupant-agent or a group-of-agent. The extension, built upon a review of the literature, introduces new elements to the framework that fall into five categories, including socio-economic, geographical location, activities, subjective values, and individual and collective adaptive actions. On-going research includes identifying occupant datasets and developing data fusion methods to generate synthetic occupants, as well as to demonstrate its applications in agent-based modeling coupled with building performance simulation.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1784298

Alternate ID(s):: OSTI ID: 1778318

Journal Information:: Automation in Construction, Journal Name: Automation in Construction Vol. 125; ISSN 0926-5805

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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