Development of a novel method to detect clothing level and facial skin temperature for controlling HVAC systems
[Display omitted] •An image classification model based on a convolutional neural network (CNN) was used to determine clothing level.•Comfortable air temperature was correlated with clothing level.•Thermal sensation was linked to facial skin temperature.•Facial skin temperature was used to control an...
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| Published in | Energy and buildings Vol. 239; p. 110859 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Lausanne
Elsevier B.V
15.05.2021
Elsevier BV |
| Subjects | |
| Online Access | Get full text |
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| Abstract | [Display omitted]
•An image classification model based on a convolutional neural network (CNN) was used to determine clothing level.•Comfortable air temperature was correlated with clothing level.•Thermal sensation was linked to facial skin temperature.•Facial skin temperature was used to control an HVAC system for a comfortable indoor environment.
People spend most of their time indoors, and thus it is important to provide occupants with a comfortable indoor thermal environment. However, inappropriate thermostat temperature settings in offices make occupants less comfortable. This study developed a new control strategy for HVAC systems that adjusts the thermostat setpoint according to clothing level and mean facial skin temperature. An image-classification model was trained on the basis of a convolutional neural network (CNN) to classify the clothing level of occupants, which was then used to calculate a comfortable air temperature. This investigation used a long-wave infrared (LWIR) camera with a face-detection program to obtain occupants’ mean facial skin temperature. This study performed experimental tests to correlate mean facial skin temperature with thermal sensation votes. The mean facial skin temperature was then used to develop a control strategy for an HVAC system in a single-occupant office. With the use of the control strategy, 91% of the subjects tested in this investigation felt thermally neutral in the office. |
|---|---|
| AbstractList | [Display omitted]
•An image classification model based on a convolutional neural network (CNN) was used to determine clothing level.•Comfortable air temperature was correlated with clothing level.•Thermal sensation was linked to facial skin temperature.•Facial skin temperature was used to control an HVAC system for a comfortable indoor environment.
People spend most of their time indoors, and thus it is important to provide occupants with a comfortable indoor thermal environment. However, inappropriate thermostat temperature settings in offices make occupants less comfortable. This study developed a new control strategy for HVAC systems that adjusts the thermostat setpoint according to clothing level and mean facial skin temperature. An image-classification model was trained on the basis of a convolutional neural network (CNN) to classify the clothing level of occupants, which was then used to calculate a comfortable air temperature. This investigation used a long-wave infrared (LWIR) camera with a face-detection program to obtain occupants’ mean facial skin temperature. This study performed experimental tests to correlate mean facial skin temperature with thermal sensation votes. The mean facial skin temperature was then used to develop a control strategy for an HVAC system in a single-occupant office. With the use of the control strategy, 91% of the subjects tested in this investigation felt thermally neutral in the office. People spend most of their time indoors, and thus it is important to provide occupants with a comfortable indoor thermal environment. However, inappropriate thermostat temperature settings in offices make occupants less comfortable. This study developed a new control strategy for HVAC systems that adjusts the thermostat setpoint according to clothing level and mean facial skin temperature. An image-classification model was trained on the basis of a convolutional neural network (CNN) to classify the clothing level of occupants, which was then used to calculate a comfortable air temperature. This investigation used a long-wave infrared (LWIR) camera with a face-detection program to obtain occupants' mean facial skin temperature. This study performed experimental tests to correlate mean facial skin temperature with thermal sensation votes. The mean facial skin temperature was then used to develop a control strategy for an HVAC system in a single-occupant office. With the use of the control strategy, 91% of the subjects tested in this investigation felt thermally neutral in the office. |
| ArticleNumber | 110859 |
| Author | Li, Xuan Chen, Qingyan |
| Author_xml | – sequence: 1 givenname: Xuan orcidid: 0000-0003-4164-4980 surname: Li fullname: Li, Xuan – sequence: 2 givenname: Qingyan surname: Chen fullname: Chen, Qingyan email: yanchen@purdue.edu |
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| Keywords | Infrared thermography Thermal comfort Clothing level Skin temperature Thermostat setpoint Image classification |
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•An image classification model based on a convolutional neural network (CNN) was used to determine clothing level.•Comfortable air... People spend most of their time indoors, and thus it is important to provide occupants with a comfortable indoor thermal environment. However, inappropriate... |
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| SubjectTerms | Air temperature Artificial neural networks Clothing level HVAC HVAC equipment Image classification Indoor environments Infrared cameras Infrared thermography Neural networks Skin Skin temperature Temperature Thermal comfort Thermal environments Thermostat setpoint |
| Title | Development of a novel method to detect clothing level and facial skin temperature for controlling HVAC systems |
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