以住户为中心的数字双胞胎:住户参与热舒适决策的案例研究

Sanaz Saadatifar, A. Sawyer, Daragh Byrne
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摘要

在开放式共享工作场所,如联合办公空间或教育工作室,不同使用者的热偏好大相径庭。随着灵活的组织工作场所战略的兴起,如何在最佳、高效的温度设定点与保持使用者的舒适度之间取得平衡成为了一项挑战。一个潜在的解决方案是深入了解室内气候的变化以及建筑使用者的决策和偏好。本文探讨了以用户为中心的数字孪生系统(OCDT)如何通过物联网温度传感器网格实时映射室内微气候来解决这一问题。在开放式工作场所中,利用大屏幕显示器来展示和可视化这些数据。这样做的目的是提高使用者的意识,并为他们提供机构,以确定符合其个人热偏好的区域。为了验证这种方法,我们进行了一项混合方法居住者研究(N = 27)。接触 OCDT 显示屏后,参与者的热满意度有所提高(p 值 = 1.269 × 10-5,显著性水平为 0.05)。OCDT 的新颖之处在于将数字孪生技术的使用范围从设施管理者扩展到居住者,即使在居住者无法直接控制恒温器的建筑物中,他们也可以解决舒适度问题。这种方法为未来铺平了道路,在未来,开放式工作空间的使用者可以在知情的情况下决定在哪里工作,以及如何在这些选择中实现热舒适度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Occupant-Centric Digital Twin: A Case Study on Occupant Engagement in Thermal Comfort Decision-Making
In open and shared workplaces, like co-working spaces or educational studios, thermal preferences vary widely among occupants. With the rise of flexible organizational workplace strategies, the challenge lies in balancing optimum, efficient temperature setpoints with maintaining occupants’ comfort. A potential solution involves a deeper understanding of variations in indoor climate and building occupants’ decision-making and preferences. This paper explores how an Occupant-Centric Digital Twin (OCDT) might address this by mapping indoor microclimates through a grid of IoT temperature sensors in real time. A large-screen display is utilized to present and visualize these data in an open workplace. The goal is to enhance awareness and provide agency for occupants to identify zones that align with their individual thermal preferences. A mixed-method occupant study (N = 27) was conducted to validate the approach. Exposure to the OCDT display resulted in higher thermal satisfaction among participants (p-value = 1.269 × 10−5, 0.05 significance level). The novelty of OCDT lies in extending the use of digital twin technology from facility managers to occupants, by granting them the agency to address comfort issues even in buildings where they lack direct control over the thermostat. This approach paves the way for a future where occupants in open workspaces can make informed decisions about where to work and how to achieve thermal comfort in those choices.
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