Authors:
Diana Hintea
1
;
James Brusey
1
;
Elena Gaura
1
;
John Kemp
1
and
Neil Beloe
2
Affiliations:
1
Coventry University, United Kingdom
;
2
Jaguar Land Rover Ltd, United Kingdom
Keyword(s):
Equivalent Temperature, Multiple Linear Regression, Thermal Comfort, HVAC.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Environmental Monitoring and Control
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Robotics and Automation
;
Sensors Fusion
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
Equivalent Temperature is generally considered an accurate predictor for thermal comfort in car cabins. However,
direct measurement of this parameter is impractical in fielded applications. The paper presents an empirical,
multiple linear regression based approach for estimating body segment equivalent temperatures for car
cabin occupants from different sensors within the car. Body part equivalent temperature at eight segments and
cabin sensor data (air temperature, surface temperature, mean radiant temperature, humidity and solar load)
was gathered in a variety of environmental and cabin conditions. 38 experimental hours of trials in a controlled
environment and 26 experimental hours of realistic driving trials were used for training and evaluating
the estimator’s performance. The estimation errors were on average between 0.5 °C and 1.9 °C for different
body parts for trials within a controlled environment, while for trials in realistic driving scenarios they ranged
between 1 °C and
2 °C. This demonstrates that passenger body part equivalent temperature can be estimated
using a multiple linear regression from environmental sensors and leads the way to comfort driven Heating,
Ventilation and Air Conditioning control.
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