Predictive modelling

Thermal comfort

Engineering

Philosophy

Skin temperature

Biomedical engineering

Embedded system

Computer science

Environmental health

Physics

Simulation

Predictive power

Wearable computer

Epistemology

Population

Artificial intelligence

Medicine

Meteorology

Machine learning

Building and Environment

A personal comfort model is an approach to thermal comfort modeling, for thermal environmental design and control, that predicts an individual's thermal comfort response, instead of the average response of a large population. We developed personal thermal comfort models using lab grade wearable in normal daily activities. We collected physiological signals (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male adults) and environmental parameters (e.g., air temperature, relative humidity) for 2–4 weeks (at least 20 h per day). Then we trained 14 models for each subject with different machine-learning algorithms to predict their thermal preference. The results show that the median prediction power could be up to 24%/78%/0.79 (Cohen's kappa/accuracy/AUC) with all features considered. The median prediction power reaches 21%/71%/0.7 after 200 subjective votes. We explored the importance of different features on the prediction performance by considering all subjects in one dataset. When all features included for the entire dataset, personal comfort models can generate the highest performance of 35%/76%/0.80 by the most predictive algorithm. Personal comfort models display the highest prediction power when occupants' thermal sensations is outside thermal neutrality. Skin temperature measured at the ankle is more predictive than measured at the wrist. We suggest that Cohen's kappa or AUC should be employed to assess the performance of personal thermal comfort models for imbalanced datasets due to the capacity to exclude random success. • We develop personal thermal comfort models using lab grade wearable sensors. • Median prediction power of the models is 24%/78%/0.79 (Cohen's kappa/accuracy/AUC). • Developed models show strong performance outside thermal neutrality. • Ankle skin temperature is more predictive than wrist skin temperature. • Abstract.

Personal thermal comfort models with wearable sensors