High-performance formaldehyde prediction for indoor air quality assessment using time series deep learning

Abstract

Indoor air pollution resulting from volatile organic compounds (VOCs), especially formaldehyde, is a significant health concern needed to predict indoor formaldehyde concentration (C_f) in green intelligent building design. This study develops a thermal and wet coupling calculation model of porous fabric to account for the migration of formaldehyde molecules in indoor air and cotton, silk, and polyester fabric with heat flux in Harbin, Beijing, Xi’an, Shanghai, Guangzhou, and Kunming, China. The time-by-time indoor dry-bulb temperature (T), relative humidity (RH), and C_f, obtained from verified simulations, were collated and used as input data for the long short-term memory (LSTM) of the deep learning model that predicts indoor multivariate time series C_f from the secondary source effects of indoor fabrics (adsorption and release of formaldehyde). The trained LSTM model can be used to predict multivariate time series Cf at other emission times and locations. The LSTM-based model also predicted C_f with mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) that fell within 10%, 10%, 0.5, 0.5, and 0.8, respectively. In addition, the characteristics of the input dataset, model parameters, the prediction accuracy of different indoor fabrics, and the uncertainty of the data set are analyzed. The results show that the prediction accuracy of single data set input is higher than that of temperature and humidity input, and the prediction accuracy of LSTM is better than recurrent neural network (RNN). The method’s feasibility was established, and the study provides theoretical support for guiding indoor air pollution control measures and ensuring human health and safety.