Non-invasive measurements of thermal discomfort for thermal preference prediction based on occupants' adaptive behavior recognition

Yanchen Liu; Hao Li; Peiping ZhengHuijun WuZhe WangZiwei LiBorong Lin

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Non-invasive measurements of thermal discomfort for thermal preference prediction based on occupants' adaptive behavior recognition

机译：Non-invasive measurements of thermal discomfort for thermal preference prediction based on occupants' adaptive behavior recognition

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With the spread of the Internet of Things in the construction industry, non-invasive thermal adaptive behavior recognition provides a new method for the real-time assessment of the indoor thermal environment. Pioneering research on non-invasive measurements of thermal discomfort for thermal preference prediction based on occupants' adaptive behavior recognition was undertaken. Questionnaire surveys on the thermal adaptive behavior of office building users in five climatic regions of China were undertaken. From 654 valid questionnaires, 14 thermal adaptive behaviors related to thermal discomfort were clustered. Then experiments were conducted to collect thermal discomfort adaptive behavioral video data from 8 views (front, rear, left, right, left-front, right-front, left-rear, and right-rear) at two heights (1.5 m and 2 m). A large-scale dataset for thermal adaptive behavior recognition with 27,504 video samples was developed. Two adaptive behavior recognition models were constructed based on the neural network model Two-Stream Inflated 3D ConvNet (I3D) and SlowFast. The results show that there is no significant difference in the types of indoor thermal adaptive behaviors among office users in different climate zones of China. The established I3D and SlowFast recognition models adopt an end-to-end approach for recognition, which overcomes dependence on human key point recognition and enables the models to utilize more environmental information. The average prediction accuracy of both models reaches 95% or above, even when complex background and human key points are partially obscured. Additionally, the recognition time of the two models is at the millisecond level, which can support real-time thermal adaptive behavior recognition.

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《Building and environment》 |2023年第1期|109889.1-109889.14|共14页
作者
Yanchen Liu; Hao Li; Peiping ZhengHuijun WuZhe WangZiwei LiBorong Lin;
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作者单位

College of Architecture & Urban Planning, Beijing University of Technology, Beijing, 100124, China;

Department of Building Science, Tsinghua University, Beijing, 100084, China;

Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, ChinaSchool of Civil Engineering, Guangzhou University, Guangzhou, 510006, China, Guangdong Provincial Key Laboratory of Building Energy Efficiency and Application Technologies, Guangzhou University, Guangzhou, 510006, China, Department of Building Science, TsWeChat, Tencent Inc., Guangzhou, 510220, ChinaSchool of Civil Engineering, Guangzhou University, Guangzhou, 510006, ChinaSchool of Civil Engineering, Guangzhou University, Guangzhou, 510006, China, Guangdong Provincial Key Laboratory of Building Energy Efficiency and Application Technologies, Guangzhou University, Guangzhou, 510006, China;

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正文语种英语
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Non-invasive measurement; Thermal discomfort; Thermal adaptive behavior; Adaptive behavior recognition;

Non-invasive measurements of thermal discomfort for thermal preference prediction based on occupants' adaptive behavior recognition

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