Where should the thermal image sensor of a smart A/C look?-Occupant thermal sensation model based on thermal imaging data

被引:12
作者
Lyu, Junmeng [1 ]
Du, Heng [1 ]
Zhao, Zisheng [2 ]
Shi, Yongxiang [1 ]
Wang, Bo [2 ]
Lian, Zhiwei [1 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Design, Shanghai 200240, Peoples R China
[2] Guangdong Midea Air Conditioning Equipment Co Ltd, Foshan 528311, Guangdong, Peoples R China
基金
国家重点研发计划;
关键词
Thermal sensation model; Two-stage model; Thermal image sensor; Smart air conditioner; PHYSIOLOGICAL-RESPONSES; PREDICTION MODEL; SKIN TEMPERATURE; COMFORT; DIFFERENCE; WARM;
D O I
10.1016/j.buildenv.2023.110405
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Applying thermal imaging sensor to air conditioner for monitoring human thermal sensation and achieving dynamic settings may satisfy occupants' thermal needs while saving energy. The existing studies are mostly based on single-view imaging to build the model and ignore the possible differences in body surface temperature on thermal sensation response by gender, etc., which may have many limitations. Subject experiments were conducted in an artificial climate chamber to obtain subjective questionnaires and thermal images of the exposed frontal face, lateral face, top of the head, forearm, and hand dorsum of 27 subjects in this study. By applying machine learning classification algorithms and global optimal regression algorithms, the temperature collection zones that can accurately reflect the thermal sensation of both genders in each view were analyzed, and a two -stage thermal sensation assessment model applicable to multiple views was developed. Of the various imaging views, the frontal view of the face is the best, followed by the lateral view of the face, the top view of the head, and the forearm/hand dorsum view. For male and female, the mean absolute errors of the thermal sensation assessment model established were 0.41-0.49 and 0.50-0.53 thermal sensation units. In addition, gender dif-ferences were found in the response of head surface temperatures to thermal sensation. The results obtained can provide a reference for the application of thermal image sensor to smart air conditioners.
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收藏
页数:15
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共 71 条
  • [1] Energy consumption and efficiency in buildings: current status and future trends
    Allouhi, A.
    El Fouih, Y.
    Kousksou, T.
    Jamil, A.
    Zeraouli, Y.
    Mourad, Y.
    [J]. JOURNAL OF CLEANER PRODUCTION, 2015, 109 : 118 - 130
  • [2] [Anonymous], 2017, Standard 55: Thermal Environmental Conditions For Human Occupancy
  • [3] A comparative study of predicting individual thermal sensation and satisfaction using wrist-worn temperature sensor, thermal camera and ambient temperature sensor
    Aryal, Ashrant
    Becerik-Gerber, Burcin
    [J]. BUILDING AND ENVIRONMENT, 2019, 160
  • [4] Skin blood flow in adult human thermoregulation: How it works, when it does not, and why
    Charkoudian, N
    [J]. MAYO CLINIC PROCEEDINGS, 2003, 78 (05) : 603 - 612
  • [5] Machine learning driven personal comfort prediction by wearable sensing of pulse rate and skin temperature
    Chaudhuri, Tanaya
    Soh, Yeng Chai
    Li, Hua
    Xie, Lihua
    [J]. BUILDING AND ENVIRONMENT, 2020, 170 (170)
  • [6] Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology
    Chaudhuri, Tanaya
    Zhai, Deqing
    Soh, Yeng Chai
    Li, Hua
    Xie, Lihua
    [J]. ENERGY AND BUILDINGS, 2018, 166 : 391 - 406
  • [7] Chaudhuri T, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON SMART GRID AND SMART CITIES (ICSGSC), P72, DOI 10.1109/ICSGSC.2017.8038552
  • [8] Evaluation of meals skin temperature formulas by infrared thermography
    Choi, JK
    Miki, K
    Sagawa, S
    Shiraki, K
    [J]. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY, 1997, 41 (02) : 68 - 75
  • [9] Development of the data-driven thermal satisfaction prediction model as a function of human physiological responses in a built environment
    Choi, Joon-Ho
    Yeom, Dongwoo
    [J]. BUILDING AND ENVIRONMENT, 2019, 150 : 206 - 218
  • [10] Study of data-driven thermal sensation prediction model as a function of local body skin temperatures in a built environment
    Choi, Joon-Ho
    Yeom, Dongwoo
    [J]. BUILDING AND ENVIRONMENT, 2017, 121 : 130 - 147