An ontology to represent synthetic building occupant characteristics and behavior

Abstract

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 occupantagent 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.

Extending the Brick schema to represent metadata of occupants

“…To enhance the interoperability of obXML in broader applications and use cases across the building life cycle, Putra and Hong et al [29] proposed an extension of the original DNAS framework for representing the synthetic occupant population. The extensions introduce new elements to the existing framework that describe more elaborate characteristics of an occupantagent or a group-of-agents for agent-based modeling applications.…”

Generating synthetic occupants for use in building performance simulation

“…Other frameworks include detailed characteristics of occupants such as socio-conomic (Andrews, 2017;Kontokosta & Jain, 2015;Tsoulou et al, 2020), subjective values (Hong et al, 2020;Ortiz & Bluyssen, 2018), and activities (Hewitt et al, 2016;Malik & Bardhan, 2020). The recent update on the DNAS framework adds more components relevant to static attributes of building occupants (Chandra-Putra et al, 2021). Attributes, such as geographical location, socio-economic, and subjective values are considered as traits that are unique to occupants and determine their perceptions and adaptive behaviors in the built environment.…”

“…This study follows the existing Drivers, Needs, Actions, and Systems (DNAS) framework to provide a better representation of building occupants (see Figure 2). The framework has been recently updated with a more elaborate occupant characteristics that was drawn from previous multiple papers (Andrews, 2017;Chandra-Putra et al, 2021;Hewitt et al, 2016;Hong et al, 2020;Senick, 2015). As described in (Chandra-Putra et al, 2021), the extended DNAS framework categorizes the occupant characteristics into four groups, including socio-demographic, location, subjective values, and activities.…”

“…The framework has been recently updated with a more elaborate occupant characteristics that was drawn from previous multiple papers (Andrews, 2017;Chandra-Putra et al, 2021;Hewitt et al, 2016;Hong et al, 2020;Senick, 2015). As described in (Chandra-Putra et al, 2021), the extended DNAS framework categorizes the occupant characteristics into four groups, including socio-demographic, location, subjective values, and activities. The framework also develops further the comfort adaptive action components by dividing them into two sub-categories based on individual control or collective decisions.…”

Impact of Window-to-Wall Ratio on Heating Demand and Thermal Comfort When Considering a Variety of Occupant Behavior Profiles

“…Occupant behavior models with various levels of complexity have been developed to simulate how occupants interact with building systems. In literature, probabilistic and stochastic models gained in popularity over deterministic models, since the first ones depict occupant actions in a more accurate matter (Carlucci et al, 2020;Putra et al, 2021). In fact, occurrence probabilities of actions like occupancy, window opening, domestic hot water and appliances usage, lighting and others can be determined from monitoring campaigns.…”