Towards unsupervised learning of thermal comfort using infrared thermography

Abstract

Maintaining thermal comfort in built environments is important for occupant health, well-being, and productivity, and also for efficient HVAC system operations. Most of the existing personal thermal comfort learning methods require occupants to provide feedback via a survey to label the monitored environmental or physiological conditions in order to train the prediction models. However, the accuracy of these models usually drops after the training process as personal thermal comfort is dynamic and changes over time due to climatic variations and/or acclimation. In this paper, we present a hidden Markov model (HMM) based learning method to capture personal thermal comfort using infrared thermography of human face. We chose human face since its blood vessels has a higher density and it is not covered while performing regular activities in built environments. Learning algorithm has 3 hidden states (i.e., uncomfortably warm, comfortable, uncomfortably cool) and uses discretization for forming the observed states from the continuous infrared measurements. The approach can potentially be used for continuous monitoring of thermal comfort to capture the variations over time. We tested and validated the method in a fourday long experiment with 10 subjects and demonstrated an accuracy of 82.8% for predicting uncomfortable conditions.

Estimating country-specific space heating threshold temperatures from national gas and electricity consumption data

“…Jazizadeh and Jung (2018) propose a novel approach for which RGB video cameras are used as sensors for measuring personalized thermo-regulation states which can be used as indicators of thermal comfort. Ghahramani et al (2018) introduce a hidden Markov model (HMM) based learning method along with infrared thermography of the human face in an attempt to capture personal thermal comfort. Niemierko et al (2019) use a D-vine copula method to capture the building heating needs by using historical data on German household heating consumption and the respective building parameters.…”

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

“…With personally-owned thermal control devices such as PCS, we can trace the associated behavior back to individual occupants, creating a direct link to personal comfort. In addition to thermal control behavior, tracking occupants' physiological conditions via wearable sensors offers another convenient way of collecting additional data points about human thermal comfort, and recent studies [32][33][34] have used skin temperatures to predict individuals' thermal comfort. However, no studies have used records of occupant behavior with personally-owned thermal control devices for individuals' comfort predictions.…”

A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning

“…The infrared sensor was mounted on the frame of eyeglasses. Based on this, a hidden Markov model was constructed to capture personal thermal sensation [20].…”