Modeling of subway indoor air quality using Gaussian process regression

Abstract

Soft sensor modeling of indoor air quality (IAQ) in subway stations is essential for public health. Gaussian process regression (GPR), as an efficient nonlinear modeling method, can effectively interpret the complicated features of industrial data by using composite covariance functions derived from base kernels. In this work, an accurate GPR soft sensor with the sum of squared-exponential covariance function and periodic covariance function is proposed to capture the time varying and periodic characteristics in the subway IAQ data. The results demonstrate that the prediction performance of the proposed GPR model is superior to that of the traditional soft sensors consisting of partial least squares, back propagation artificial neural networks, and least squares support vector regression (LSSVR). More specifically, the values of root mean square error, mean absolute percentage error, and coefficient of determination are improved by 12.35%, 9.53%, and 40.05%, respectively, in comparison with LSSVR.