An ontology to represent synthetic building occupant characteristics and behavior
An ontology to represent synthetic building occupant characteristics and behavior
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DOI：
10.1016/j.autcon.2021.103621
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发表时间：
2021-05
影响因子：
10.3
通讯作者：
Handi Chandra Putra;Tianzhen Hong;C. Andrews
中科院分区：
文献类型：
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作者：
Handi Chandra Putra;Tianzhen Hong;C. Andrews
自2013年引入乘客行为驱动需求系统（DNAS）框架以来，研究人员已经使用该框架或根据他们的案例研究对其进行进一步开发，其中包括收集有关乘客行为的新数据。由于添加的新数据点相对较少，因此这项工作的成本往往很高。当已经收集的数据不符合建模者的互操作性要求时，就会出现问题。以前的研究通过开发更复杂的本体来解决这个问题，这些本体能够与其他数据集和合成数据方法集成，以满足独特的研究应用。本文提出了一种扩展的DNAS框架的合成占用者数据的表示，以支持各种应用程序和用例在整个建筑生命周期。一个基于代理的建模应用程序是我们的动机之一，需要更详细的特性占领代理或一组代理。扩展，建立在对文献的审查，引入新的要素，分为五个类别，包括社会经济，地理位置，活动，主观价值观，个人和集体的适应行动的框架。正在进行的研究包括识别居住者数据集和开发数据融合方法来生成合成居住者，以及展示其在基于代理的建模与建筑性能模拟中的应用。
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.