The Personalized Thermal Comfort Prediction Using an MH-LSTM Neural Network Method

Abstract

As demand for indoor thermal comfort increases, occupants' subjective thermal sensation is becoming an important indicator of the building environment. Traditional models like the predicted mean vote-based model may not be reliable for individual comfort. This study proposed the multihead long short-term memory (LSTM) model to reflect physical and environment-driven data variation. Controlled experiments were conducted with individual temperature measurements of six participants, and the collected data showed significant potential to predict individual thermal comfort using a model trained for each person. The results derived from this study can be utilized, in future, for predicting the thermal comfort and for optimizing the thermal environments using personal body temperature and surrounding environmental data in a space where mainly independent activities are performed. This study contributes to the relevant literature by suggesting a method that predicts thermal comfort based on the multihead LSTM method.