Vision-based estimation of clothing insulation for building control: A case study of residential buildings

被引:32
作者
Choi, Haneul [1 ]
Na, HooSeung [1 ]
Kim, Taehung [2 ]
Kim, Taeyeon [1 ]
机构
[1] Yonsei Univ, Dept Architecture & Architectural Engn, Seoul 03722, South Korea
[2] Hyundai Mobis Co, Driving Image Recognit Cell, Gyeonggi Do 16891, South Korea
基金
新加坡国家研究基金会;
关键词
Clothing insulation; Thermal comfort; Computer vision; Convolutional neural network; Deep learning; Occupant-centric control; THERMAL COMFORT; BEHAVIOR; WEATHER;
D O I
10.1016/j.buildenv.2021.108036
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Efforts have been made to estimate clothing insulation in real time, an element of thermal comfort for occupants. Nevertheless, an effective method to estimate clothing insulation in real time is lacking. In addition, there has been little debate on how to apply clothing insulation to building control in practice. The purpose of this study is to propose a method for estimating clothing insulation using deep learning-based vision recognition, which has recently attracted attention and implement building control based on clothing insulation. The study also evaluates the significance of the method in effective building control. The results demonstrated that the proposed framework, CloNet, showed an accuracy of 94% for the validation image dataset and 86% for the actual built environment. In addition, we proved that the proposed vision-based estimation method is very fast and practical for estimating clothing insulation. The control experiment showed that the CloNet-based predicted mean vote (PMV) control changed the set temperature in response to changes in the subject's clothing. Compared to the traditional PMV control, the CloNet-based PMV control improved the thermal preference and thermal comfort vote. These results prove that clothing insulation estimation can be useful for building control.
引用
收藏
页数:14
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  • [1] A.S. for T. and Materials, 2015, F1291 ASTM
  • [2] [Anonymous], 2007, 152512007 INDOOR ENV
  • [3] [Anonymous], 2005, THERM ENV DET INT TH
  • [4] [Anonymous], 2013, 552013 ENV COND HUM
  • [5] HVAC systems testing and check: A simplified model to predict thermal comfort conditions in moderate environments
    Buratti, C.
    Ricciardi, P.
    Vergoni, M.
    [J]. APPLIED ENERGY, 2013, 104 : 117 - 127
  • [6] OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
    Cao, Zhe
    Hidalgo, Gines
    Simon, Tomas
    Wei, Shih-En
    Sheikh, Yaser
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 43 (01) : 172 - 186
  • [7] Cho J.H., 2013, ECOL ARCHIT ENV, V13, P111
  • [8] Using the contrast within a single face heat map to assess personal thermal comfort
    Cosma, Andrei Claudiu
    Simha, Rahul
    [J]. BUILDING AND ENVIRONMENT, 2019, 160
  • [9] Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions
    Dai, Changzhi
    Zhang, Hui
    Arens, Edward
    Lian, Zhiwei
    [J]. BUILDING AND ENVIRONMENT, 2017, 114 : 1 - 10
  • [10] Appliance classification using VI trajectories and convolutional neural networks
    De Baets, Leen
    Ruyssinck, Joeri
    Develder, Chris
    Dhaene, Tom
    Deschrijver, Dirk
    [J]. ENERGY AND BUILDINGS, 2018, 158 : 32 - 36