Real-time and contactless measurements of thermal discomfort based on human poses for energy efficient control of buildings

Individual thermal discomfort perception gives important feedback signals for energy efficient control of building heating, ventilation and air conditioning systems. However, there is few effective ...

[1]  Farrokh Jazizadeh,et al.  Personalized thermal comfort inference using RGB video images for distributed HVAC control , 2018, Applied Energy.

[2]  Gary Higgins,et al.  Real-time prediction model for indoor temperature in a commercial building , 2018, Applied Energy.

[3]  Krishna R. Pattipati,et al.  Predicting individual thermal comfort using machine learning algorithms , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[4]  Z. Lian,et al.  Evaluation of calculation methods of mean skin temperature for use in thermal comfort study , 2011 .

[5]  Iakovos Michailidis,et al.  Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule , 2015 .

[6]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[7]  Yi Jiang,et al.  A data-driven method to describe the personalized dynamic thermal comfort in ordinary office environment: From model to application , 2014 .

[8]  S. Matsumoto,et al.  Prediction of whole-body thermal sensation in the non-steady state based on skin temperature , 2013 .

[9]  Roberto Lamberts,et al.  A review of human thermal comfort in the built environment , 2015 .

[10]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[11]  Joyce Kim,et al.  Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning , 2018 .

[12]  Lihua Xie,et al.  Machine learning based prediction of thermal comfort in buildings of equatorial Singapore , 2017, 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC).

[13]  Weiwei Liu,et al.  A neural network evaluation model for individual thermal comfort , 2007 .

[14]  Bo Peng,et al.  Data-Driven Thermal Comfort Prediction With Support Vector Machine , 2017 .

[15]  Hui Zhang,et al.  The skin's role in human thermoregulation and comfort , 2006 .

[16]  Takuji Suzuki,et al.  Estimation of thermal sensation using human peripheral skin temperature , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[17]  Hui Zhang,et al.  Observations of upper-extremity skin temperature and corresponding overall-body thermal sensations and comfort , 2007 .

[18]  Burcin Becerik-Gerber,et al.  Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort , 2016 .

[19]  Lihua Xie,et al.  Thermal comfort prediction using normalized skin temperature in a uniform built environment , 2018 .

[20]  Yaser Sheikh,et al.  Hand Keypoint Detection in Single Images Using Multiview Bootstrapping , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Burcin Becerik-Gerber,et al.  An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling , 2015 .

[22]  Zhaojun Wang,et al.  Thermal adaptation in overheated residential buildings in severe cold area in China , 2017 .

[23]  Farrokh Jazizadeh,et al.  Vision-based thermal comfort quantification for HVAC control , 2018, Building and Environment.

[24]  Issam El Naqa,et al.  Prediction of the thermal comfort indices using improved support vector machine classifiers and nonlinear kernel functions , 2016 .

[25]  Farrokh Jazizadeh,et al.  Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions , 2019, Applied Energy.

[26]  Yaser Sheikh,et al.  OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Zoltán Nagy,et al.  Using machine learning techniques for occupancy-prediction-based cooling control in office buildings , 2018 .

[28]  Hui Zhang,et al.  Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions , 2017 .

[29]  Jinqing Peng,et al.  Using Upper Extremity Skin Temperatures to Assess Thermal Comfort in Office Buildings in Changsha, China , 2017, International journal of environmental research and public health.

[30]  Kwang Suk Park,et al.  Estimation of Thermal Sensation Based on Wrist Skin Temperatures , 2016, Sensors.

[31]  Burcin Becerik-Gerber,et al.  Towards unsupervised learning of thermal comfort using infrared thermography , 2018 .

[32]  Iakovos Michailidis,et al.  Joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids , 2015 .

[33]  Y Yao,et al.  Heart rate variation and electroencephalograph--the potential physiological factors for thermal comfort study. , 2009, Indoor air.

[34]  P. Fanger.  Moderate Thermal Environments Determination of the PMV and PPD Indices and Specification of the Conditions for Thermal Comfort , 1984 .

[35]  S Schiavon,et al.  Thermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized persons , 2017, Indoor air.

[36]  Carol C. Menassa,et al.  A Personalized HVAC Control Smartphone Application Framework for Improved Human Health and Well-Being , 2017 .

[37]  Guoqing Liu,et al.  A pilot study of online non-invasive measuring technology based on video magnification to determine skin temperature , 2017 .

[38]  Varun Ramakrishna,et al.  Convolutional Pose Machines , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[40]  Richard de Dear,et al.  Individual difference in thermal comfort: A literature review , 2018, Building and Environment.