L773:1996 3599 OR L773:1996 8744
Sökning: L773:1996 3599 OR L773:1996 8744 > High-performance fo...
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- Lu, LiuXi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian, Peoples R China. (författare)
High-performance formaldehyde prediction for indoor air quality assessment using time series deep learning
- Artikel/kapitelEngelska2024
Förlag, utgivningsår, omfång ...
- 2024
- TSINGHUA UNIV PRESS,2024
- printrdacarrier
Nummerbeteckningar
- LIBRIS-ID:oai:DiVA.org:mdh-65675
- https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-65675URI
- https://doi.org/10.1007/s12273-023-1091-4DOI
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- Språk:engelska
- Sammanfattning på:engelska
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- SwePubSwePub
Klassifikation
Anmärkningar
- 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.
Ämnesord och genrebeteckningar
- NATURVETENSKAP Annan naturvetenskap hsv//swe
- NATURAL SCIENCES Other Natural Sciences hsv//eng
- multivariate time series
- formaldehyde concentration
- deep learning
- heat-humidity coupling
- mass transfer
- secondary source effect
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Huang, XinyuXi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian, Peoples R China. (författare)
- Zhou, XiaojunXi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian, Peoples R China. (författare)
- Guo, JunfeiXi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian, Peoples R China. (författare)
- Yang, XiaohuXi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian, Peoples R China. (författare)
- Yan, Jinyue,1959-Mälardalens universitet,Framtidens energi,Hong Kong Polytech Univ, Dept Bldg Environm & Energy Engn, Kowloon, Hong Kong, Peoples R China.(Swepub:mdh)jyn01 (författare)
- Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian, Peoples R China.Framtidens energi (creator_code:org_t)
Sammanhörande titlar
- Ingår i:Building Simulation: TSINGHUA UNIV PRESS1996-35991996-8744
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- Guo, Junfei
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