Bayesian meta-learning for personalized thermal comfort modeling

Abstract

Data-driven models coupled with machine learning techniques have been developed to predict thermal comfort of individuals. However, collecting (quantitatively and qualitatively) sufficient data to develop and train the models is often challenging. This paper presents a Bayesian meta-learning approach for developing reliable, data-driven personalized thermal comfort models using limited data from individuals. The learning process considers general thermal comfort impact factors (environmental variables, clothing level and metabolic rate) as well as personal thermal characteristics. The personal thermal characteristics are expressed as a vector of continuous latent variables, estimated using limited data from each person. A high-dimensional neural network was developed to map model inputs (e.g., air temperature, relative humidity) and the vector of the continuous latent variables with personal thermal sensation (model output). The model parameters in the neural network are trained with data from various people using a subset of the ASHRAE RP-884 database. The neural network is transferrable without any update or modification (i.e., the same trained network can be used to predict the thermal preference of new individuals given their personal thermal characteristics), making the learning approach data-efficient. The results show that the developed Bayesian meta-learning approach to infer personal thermal comfort performs better than existing methods, especially when using limited data. This is important considering the practical limitations in collecting sufficient thermal response data from individuals in real buildings.