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Information match between continuous occupant data streams and one-time manual surveys on indoor climate

机译：在室内气候中连续乘员数据流和一次性手动调查之间的信息匹配

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Occupant-centric data streams, and more specifically continuous subjective occupant feedback (CSOF) systems, offer the possibility for autonomous collection of occupant feedback in buildings. They are made possible by recent developments in pervasive ICT technology and can enable a continuous flow of information that may enhance human-centric building design and operation. Due to the relative novelty of these systems, no research has been developed so far to systematically evaluate whether information collected by CSOF systems is truly representative of the entire population's opinions and evaluations. In this study, we analyze how information on occupant's opinions on indoor climate collected though a multi-level CSOF system compare to the information obtained though simultaneously performed manual surveys. We used data collected from five field tests in modern office buildings with uninformed occupants, and compare a total of 317 Satisfaction evaluations, 124 Complaints, and 44 Control actions with 546 surveys. Using logistic regression techniques, we investigated the relations between the feedback information and the information from surveys. We found that cumulative link models were suitable for modeling the relationship between feedback and survey data. The Building ID tag was the most important variable for modeling Occupant satisfaction and Occupant complaint feedback. Occupant control actions was best modeled using the Workplace ID. When comparing CSOF with surveys, we found a Mean Absolute Error (MAE) of 16% and of 12%, for Occupant satisfaction and for Occupant complaint feedback, respectively. We demonstrated that the adopted methods are suitable for understanding the meaning of the collected CSOF data. Further studies based on this methodology and using a larger dataset should be carried out to deepen the understanding of CSOF feedback significance and to increase the soundness of the results obtained in this study.
机译：以乘客为中心的数据流，更具体地说是连续的主观乘员反馈（CSOF）系统，提供了自主收集建筑物的反馈。通过普遍的ICT技术的最新发展，它们可以实现，可以连续的信息流，这些信息可以增强以人用于以人为本的建筑设计和操作。由于这些系统的相对新颖性，到目前为止没有开发研究，以系统地评估CSOF系统收集的信息是否真正代表整个人口的意见和评估。在这项研究中，我们分析了乘员对室内气候的意见的信息如何与多级CSOF系统相比同时执行手动调查。我们使用了现代办公楼中的五个现场测试中收集的数据，其中包含不知情的居住者，并比较317个满意度评估，124名投诉和44个控制行动，546个调查。使用Logistic回归技术，我们调查了反馈信息与来自调查的信息之间的关系。我们发现累积链接模型适用于建模反馈和调查数据之间的关系。构建ID标记是为占用者满意度和占用投诉反馈建模最重要的变量。乘员控制操作是使用工作场所ID建模的最佳建模。在将CSOF与调查进行比较时，我们发现一个平均绝对误差（MAE）为16％和12％，分别用于占用者满意度和占用者投诉反馈。我们证明采用的方法适用于理解收集的CSOF数据的含义。基于该方法和使用较大数据集的进一步研究应进行，以加深对CSOF反馈意义的理解，并增加本研究中获得的结果的声音。

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