Environmental and human data-driven model based on machine learning for prediction of human comfort

Abstract

Occupants' comfort level has a strong correlation with health problems. Providing a comfortable environment for the occupants will bring the benefits of improved health. To achieve this goal, it is necessary to have a reliable human comfort model for predicting the occupants' comfort level and subsequently controlling the involved comfort condition. However, the comfort perception of occupants is subjective. There is a lack of objective indices for measuring comfort level. Furthermore, human comfort is affected by various environmental factors. Such situations make it difficult to set up a model for measuring human comfort. To address the challenges, we use Blood Pulse Wave (BPW) as an objective comfort index and adopt a data-driven approach to predict human comfort level based on data including both environmental factors and human factors. We propose a framework for collecting the data followed by investigating the relationship between the factors with the purpose of building a scalable comfort model. In consideration of the nonlinear relationship present in the dataset, we opt for support vector regression with radial basis function (SVR-RBF) algorithm to establish the comfort model. To validate the predication performance of this method, we have applied the other six popular machine learning models on the same dataset. In order to choose an optimal model, we apply the holdout method and k-folder cross-validation method together with the grid search. The comparison results show that the SVR-RBF has the best performance for comfort prediction according to the mean squared error, mean absolute error and R-squared score.