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

作　　者：Liu Lu Xinyu Huang Xiaojun Zhou Junfei Guo Xiaohu Yang Jinyue Yan 

机构地区：[1]School of Human Settlements and Civil Engineering,Xi’an Jiaotong University,Xi’an,China [2]Department of Building Environment and Energy Engineering,The Hong Kong Polytechnic University,Kowloon,Hong Kong,China [3]Future Energy Profile,School of Business,Society,and Engineering,Mälardalen University,Västerås,Sweden

出　　处：《Building Simulation》2024年第3期415-429,共15页建筑模拟（英文）

基　　金：This work was supported by the National Natural Science Foundation of China(52278129);the Key Scientific and Technological Innovation Team of Shaanxi Province(2023-CX-TD-29);Xiaohu Yang greatly acknowledged the support by the K.C.Wong Education Foundation.

摘　　要：Indoor air pollution resulting from volatile organic compounds(VOCs),especially formaldehyde,is a significant health concern needed to predict indoor formaldehyde concentration(Cf)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 Cf,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 Cf 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 Cf 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.

关 键 词：multivariate time series formaldehyde concentration deep learning heat-humidity coupling mass transfer secondary source effect 

分 类 号：X51[环境科学与工程—环境工程]