The Changing Socioeconomic Context of Buildings

Andrews, Clinton J.

doi:10.1115/1.4034911

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…The Drivers-Needs-Actions-Systems (DNAS) framework environment includes actions such as thermostat adjustment, lighting adjustment, fans and portable heater operation, and moving between spaces. 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).…”

Section: Occupant Data Ontologymentioning

confidence: 99%

“…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.…”

Section: Data Ontologymentioning

confidence: 99%

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Generating synthetic occupants for use in building performance simulation

Putra

Andrews

Hong

2021

Journal of Building Performance Simulation

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Occupant behavior simulation frameworks can employ synthetic populations to characterize occupancy and behavioral patterns in buildings based on real demographic data at a certain geographical location. Multiple methods are available to generate a synthetic population, with pros-and cons-for different applications and contexts. 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. 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. The resulting synthetic occupant data is evaluated by comparing the joint distributions between the actual and synthetic data sets, % difference, and Standardized-Root-Mean-Squared-Error (SRMSE). 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 the best performance compared to the cases of office and senior housing. Future work includes testing and demonstration of how the synthetic data sets feed to occupant behavior module through co-simulation with building performance simulation tools, such as EnergyPlus.

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Section: Occupant Data Ontologymentioning

confidence: 99%

Section: Data Ontologymentioning

confidence: 99%

Generating synthetic occupants for use in building performance simulation

Putra

Andrews

Hong

2021

Journal of Building Performance Simulation

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“…Higher-level OB data often includes demographic, economic, and social attributes of consumption [11]- [14]. Other disciplines, including environmental psychology [15], sociology [10], and urban planning [11], [16] have undertaken similar efforts The most ambitious to date are the ASHRAE Thermal Comfort Database and ASHRAE Global Thermal Comfort Database [17], [18]. Even these ambitious, valuable efforts are extremely limited in scope, focusing only on a selection of thermal comfort aspects of OB.…”

Section: Introductionmentioning

confidence: 99%

An ontology to represent synthetic building occupant characteristics and behavior

Putra

Hong

Andrews

2021

Automation in Construction

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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.

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“…The issue of generalizability has motivated researchers to develop larger, multi-building databases that could provide a more robust basis for OB modeling (International Energy Agency, 2017;Licina et al, 2018). Higher-level OB data often includes demographic, economic, and social attributes (Andrews, 2017;Kontokosta & Jain, 2015). The most ambitious effort to date is the development of ASHRAE Thermal Comfort Database 1 and 2 (short name: comfort database) (Licina et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The paper also describes a workflow of using the ontology to generate a synthetic occupant population. Population synthesis has not been very popular in building research (Andrews, 2017). Population synthesis has grown in popularity in other disciplines, particularly in transportation (Williamson et al, 1998), public health (Beckman et al, 1996;E.…”

Section: Introductionmentioning

confidence: 99%

Application of the DNAS framework expansion to occupant population synthesis

Putra¹,

Hong²

2021

Building Simulation Conference Proceedings

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Research in occupant behaviour is now using a more elaborate framework of building occupant interaction. Researchers often face challenges in collecting data, particularly for the data to meet the minimum number of required data points and the data interoperability requirements. Researchers address the first issue with the synthetic population and the latter with data ontologies. While synthetic population is commonly used to address the first issue, data ontology development is used to address the latter. The two solutions are complementary to each other. One of the known ontologies in building occupant behaviour research is the Drivers-Needs-Actions-Systems (DNAS) ontology, which has been used by building modelers to describe energy-related occupant behaviour. This paper describes the ontology-based synthetic population generation that can be used in the agent-based modeling (ABM) applications. This paper considers multiple data sources, including ASHRAE Thermal Comfort DB II and IEA Annex 66 data sets. A case study of an office building is used to present the workflow of DNAS framework expansion, synthetic population generation, and agent-based modeling. Key Innovations• The expansion of occupant data ontology, namely DNAS framework that includes a more elaborate occupant characteristics and its use in population synthesis. • Population synthesis framework using Bayesian Networks approach. • An application of synthetic occupant population generation in building simulation research using a case study of an office building Practical ImplicationsThe expansion of occupant data ontology, namely DNAS framework, supports occupant data collection effort in the building life cycle. Another use case of the effort is to inform occupant population synthesis, which is largely motivated several reasons. The main motivation is to pave the increasing the increasing need of a more elaborate data on real occupants while maintaining anonymity. Population synthesis is considered as the right approach in other disciplines, such as in transportation and urban planning, yet, still rare in the building simulation research.In the building energy simulation and occupant research, population intends to support modeling work, especially those that use agent-based modeling (ABM) approach, where each agent represents a real individual occupant.

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The Changing Socioeconomic Context of Buildings

Cited by 4 publications

References 23 publications

Generating synthetic occupants for use in building performance simulation

Generating synthetic occupants for use in building performance simulation

An ontology to represent synthetic building occupant characteristics and behavior

Application of the DNAS framework expansion to occupant population synthesis

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