Influence of psychological and personal factors on predicting individual's thermal comfort in an office building using linear estimation and machine learning model

Thermal comfort in the building affects occupants' health, productivity, and electricity use. Predicting occupants' thermal comfort in advance will be helpful. Nowadays, a widely used model for thermal comfort prediction is the predicted mean vote (PMV). However, studies have found discrepancies between PMV and thermal sensation votes. In this paper, a comparative study was made by developing thermal comfort prediction in office building models using linear estimation and machine learning (ML) algorithms. Fanger's six parameters and nine other parameters are considered for linear estimation and ML input parameters. These parameters are divided into two groups: the psychology group with the parameter's thermal preference, thermal acceptance, overall comfort, air movement vote, humidity preference, and humidity vote, and the personal group with the parameters of gender and age. The predictive model developed showed the validated coefficient of correlation of more than 0.88 for both categories. For ML, training and evaluation have been done for five ML models: Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbour (KNN). The findings showed that new parameters made significantly better thermal comfort prediction. RF has the lowest prediction average error, 0.706 when including all the psychological group parameters.