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

Building Simulation 2024年第3期17卷 415-429页

作者： Liu Lu Xinyu Huang Xiaojun Zhou Junfei Guo Xiaohu Yang Jinyue Yan School of Human Settlements and Civil Engineering Xi’an Jiaotong UniversityXi’anChina Department of Building Environment and Energy Engineering The Hong Kong Polytechnic UniversityKowloonHong KongChina Future Energy Profile School of BusinessSocietyand EngineeringMälardalen UniversityVästeråsSweden

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 *** 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,*** 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 *** 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,*** addition,the characteristics of the input dataset,model parameters,the prediction accuracy of different indoor fabrics,and the uncertainty of the data set are *** 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

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