Thermal Comfort-Based Personalized Models with Non-Intrusive Sensing Technique in Office Buildings

Siliang Lu,Erica Cochran Hameen,Shihan Wang,Weilong Wang

doi:10.3390/app9091768

Abstract

Heating, ventilation and air-conditioning (HVAC) systems play a key role in shaping the built environment. However, centralized HVAC systems cannot guarantee the provision of a comfortable thermal environment for everyone. Therefore, a personalized HVAC system that aims to adapt thermal preferences has drawn much more attention. Meanwhile, occupant-related factors like skin temperature have not had standardized measurement methods. Therefore, this paper proposes to use infrared thermography to develop individual thermal models to predict thermal sensations using three different feature sets with the random forest (RF) and support vector machine (SVM). The results have shown the correlation coefficients between clothing surface temperature and thermal sensation are 11% and 3% higher than those between skin temperature and thermal sensation of two subjects, respectively. With cross-validation, SVM with a linear kernel and penalty number of 1, as well as RF with 50 trees and the maximum tree depth of 3 were selected as the model configurations. As a result, the model trained with the feature set, consisting of indoor air temperature, relative humidity, skin temperature and clothing surface temperature, and with linear kernel SVM has achieved 100% recall score on test data of female subjects and 95% recall score on that of male subjects.

Highlights

Most people spend 90% of their time indoors [1] since buildings can provide satisfactory environments for human beings
The model trained with the feature set, consisting of indoor air temperature, relative humidity, skin temperature and clothing surface temperature, and with linear kernel support vector machine (SVM) has achieved 100% recall score on test data of female subjects and 95% recall score on that of male subjects
In order to improve prediction performance of thermal comfort, individual thermal models for female subject and male subject were developed with SVM and random forest (RF) based on a six day experimental study conducted in an open-plan office in Shanghai

Summary

Introduction

Most people spend 90% of their time indoors [1] since buildings can provide satisfactory environments for human beings. Open-plan office buildings are faced with problems like unsatisfactory shared indoor temperature and humidity due to different occupants’. Ghahramani et al proposed a wearable infrared eyeglass frame to measure skin temperature at different points of the skin, including nose, front face, back of ear and cheek bone so as to infer thermal preferences [18]. Ranjan and Scott [24] have used IR cameras to dynamically detect and predict thermal comfort They classified thermal preferences based on skin temperature of forehead, cheek, jaws, upper neck, lower neck, palm core, palm and back of hand, and found that the face outperformed other body regions. An empirical study utilized an infrared (IR) sensor called Lepton to estimate occupant thermal comfort level by measuring skin temperature measured from different face regions. The results have shown that ears, nose and cheeks are most indicative to thermal comfort [26]

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