An agent-based model of building occupant behavior during load shedding

Putra, Handi Chandra; Andrews, Clinton J.; Senick, Jennifer

doi:10.1007/s12273-017-0384-x

Cited by 35 publications

(16 citation statements)

References 29 publications

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“…[5], [48] 2.1 Comfort features Literature in building energy use and occupant behavior has grown much in the last decade.…”

Section: Representation Of the Synthetic Occupant Populationmentioning

confidence: 99%

“…In office buildings, for example, building occupants often have limited access to specific control and adjustments, such as overhead lighting, operable windows, movable shades, and thermostat. A tenant representative plays a mediator role to transmit the occupants' requests to the building manager, who possesses direct access to the controls [48], [76]. Similarly, in residential building units with rather decentralized control, the head of a household often gains more access to this control than the rest of the members in the same household.…”

Section: Collective Adaptive Actionsmentioning

confidence: 99%

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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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“…[5], [48] 2.1 Comfort features Literature in building energy use and occupant behavior has grown much in the last decade.…”

Section: Representation Of the Synthetic Occupant Populationmentioning

confidence: 99%

Section: Collective Adaptive Actionsmentioning

confidence: 99%

An ontology to represent synthetic building occupant characteristics and behavior

Putra

Hong

Andrews

2021

Automation in Construction

Self Cite

View full text Add to dashboard Cite

show abstract

“…Top-down, equation-based modeling frameworks Robinson 2009, 2010), which are the most tractable to implement as part of BPS program, cannot explicitly represent the causal structures that yield behavioral diversity across individuals and populations. Agentbased models (ABMs) (Azar and Menassa 2012;Lee and Malkawi 2014b;Putra et al 2017), which can simulate individual-level decision-making processes for multiple behaviors at once and social interactions, offer greater flexibility in exploring causality; however, these models require more resources to develop and implement in BPS programs (e.g., more data, modeler time, computing power).…”

Section: Modeling the Human-building Interaction For Occupant-centricmentioning

confidence: 99%

“…Agent-based models, though not a focus of the Annex, have continued to grow in use throughout the occupant behavior modeling community. Most ABMs have been used in an exploratory fashion without validation efforts, e.g., (Papadopoulos and Azar, 2016;Putra et al, 2017); attempts at ABM validation have shown promising predictive capabilities, but for limited occupant samples .…”

Section: Modeling the Human-building Interaction For Occupant-centricmentioning

confidence: 99%

Building simulation: Ten challenges

2018

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“…Adaptive thermal comfort, which relies on changing clothing levels to gain a wider comfortable temperature range has been also widely researched [51]. Clothing levels cannot be sensed electronically and therefore surveys and observations have been commonly used [41] and in recent years methodological advances have been developed that make dynamic simulations of building-occupant systems more tractable, such as visualization systems [52] agent-based models [53] or adaptive building simulations [54] among others. However, a significant problem when simulating the performance of a building is still the precise determination of the complete requirements of the models of the behavior of the occupants [55,56].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influence Subjective Human Parameters in the Calculation of Thermal Comfort and Energy Consumption of Buildings

et al. 2019

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In the present work, we analyze the influence of the designer’s choice of values for the human metabolic index (met) and insulation by clothing (clo) that can be selected within the ISO 7730 for the calculation of the energy demand of buildings. To this aim, we first numerically modeled, using TRNSYS, two buildings in different countries and climatologies. Then, we consistently validated our simulations by predicting indoor temperatures and comparing them with measured data. After that, the energy demand of both buildings was obtained. Subsequently, the variability of the set-point temperature concerning the choice of clo and met, within limits prescribed in ISO 7730, was analyzed using a Monte Carlo method. This variability of the interior comfort conditions has been finally used in the numerical model previously validated, to calculate the changes in the energy demand of the two buildings. Therefore, this work demonstrated that the diversity of possibilities offered by ISO 7730 for the choice of clo and met results, depending on the values chosen by the designer, in significant differences in indoor comfort conditions, leading to non-negligible changes in the calculations of energy consumption, especially in the case of big buildings.

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An agent-based model of building occupant behavior during load shedding

Cited by 35 publications

References 29 publications

An ontology to represent synthetic building occupant characteristics and behavior

An ontology to represent synthetic building occupant characteristics and behavior

Building simulation: Ten challenges

Analysis of the Influence Subjective Human Parameters in the Calculation of Thermal Comfort and Energy Consumption of Buildings

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