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An agent-based simulation of occupancy schedule in office buildings

机译：办公楼中基于代理的占用时间表模拟

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摘要

Occupant behavior is one of the most influential parameters affecting buildings' energy consumption. However, simplifying this parameter in building performance simulation tools can result in discrepancies between actual buildings and simulations. Because occupant behavior differs from individual to individual, the energy performance of a building is significantly related to its occupants. The present study proposes a novel agent-based occupancy simulation model to accurately simulate stochastic occupancy schedules in a real-time scenario. Instead of using sensory or time-use data, it considers occupancy parameters, including occupants' presence, location, and interaction with the building. The main contribution of this study is the proposal of a scalable simulation-based tool for predicting occupancy schedules in office buildings that does not require historical occupancy data. The developed framework is then implemented in an example that determines the stochastic occupancy schedule of an office building. The performance of lighting occupancy sensors is also modeled as an application of the simulated occupancy schedule. The results indicate that installing occupancy sensors in the office building reduced lighting energy consumption by 24-33%. It also appeared that lighting occupancy sensors may be more beneficial in buildings with fewer occupants. The proposed model can be used by researchers and practitioners to better understand the elements of the occupancy schedules in office buildings, as well as to analyze the impact of occupants on buildings' spaces and energy systems.
机译：占用行为是影响建筑物能耗的最具影响力的参数之一。但是，在构建性能仿真工具中简化此参数可能导致实际建筑物和模拟之间的差异。由于占用行为与个人的不同之处不同，建筑物的能量性能与其居住者显着相关。本研究提出了一种新的基于代理的占用仿真模型，可以在实时方案中精确地模拟随机占用时间表。它而不是使用感官或时间使用数据，而是考虑占用参数，包括占用者的存在，位置和与建筑物的互动。本研究的主要贡献是提出可扩展模拟的工具，用于预测在不需要历史占用数据的办公楼中的占用时间表。然后在确定办公楼的随机占用时间表的示例中实施开发的框架。照明占用传感器的性能也被建模为模拟占用时间表的应用。结果表明，在办公室建筑中安装占用传感器将照明能耗降低24-33％。它还似乎在具有较少居住者的建筑物中可能更有益。拟议的模型可以由研究人员和从业者使用，以更好地了解办公楼中的入住时间表的要素，以及分析占用者对建筑物空间和能源系统的影响。

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