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Development of a systematic procedure to establish customized shading behavior identification model

机译:建立定制阴影行为识别模型的系统程序的开发

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Occupant behavior is becoming a major factor impacting indoor environments and building operations. Individual differences in indoor environment preferences impact the implementation of occupant-centric building operations. Previous studies regarding occupant behavior have focused on improving the building simulation performance by establishing a representative occupant behavior model to reduce the gap between the real performance and simulation results. In this study, a customized shading behavior identification model (CSBIM) is developed to learn and identify the personalized shading behavior habits and indoor environmental preferences of targeted occupants during the building operation phase. The CSBIM can be used to extract personalized behavioral preferences and embedded into the optimization controls of occupant-centric buildings. The proposed CSBIM is developed based on the cognitive concept of subliminal perception. Monitored data from an office on campus is used to select the optimal classification algorithm and evaluate the effectiveness of the CSBIM. The results indicate that using CSBIM & ndash; Random Forest improved capturing the personalized shading behavior by 12.56%. Finally, the advantages and limitations of the CSBIM are also considered.(c) 2021 Elsevier B.V. All rights reserved.
机译:占用行为正在成为影响室内环境和建筑运营的主要因素。室内环境偏好的个体差异影响了以乘客为中心的建筑运营实施。以前关于占用行为的研究专注于通过建立代表性占用行为模型来改善建筑模拟性能,以减少实际性能和仿真结果之间的差距。在本研究中,开发了一种定制的阴影行为识别模型(CSBIM)以在建筑运营阶段学习和识别目标占用者的个性化遮蔽行为习惯和室内环境偏好。 CSBIM可用于提取个性化的行为偏好,并嵌入到乘员中心建筑物的优化控制中。拟议的CSBIM是基于潜意识认知的认知概念而开发的。来自校园Office的监控数据用于选择最佳分类算法,并评估CSBIM的有效性。结果表明,使用CSBIM&Ndash;随机森林改善了个性化遮蔽行为12.56%。最后,还考虑了CSBIM的优点和局限。(c)2021 Elsevier B.v.保留所有权利。

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