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

  • Research Article
  • Indoor/Outdoor Airflow and Air Quality
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Abstract

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.

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Acknowledgements

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.

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Authors and Affiliations

  1. School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an, China

    Liu Lu, Xinyu Huang, Xiaojun Zhou, Junfei Guo & Xiaohu Yang

  2. Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China

    Jinyue Yan

  3. Future Energy Profile, School of Business, Society, and Engineering, Mälardalen University, Västerås, Sweden

    Jinyue Yan

Authors
  1. Liu Lu
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  2. Xinyu Huang
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  4. Junfei Guo
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  5. Xiaohu Yang
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Contributions

Liu Lu: methodology, analysis, and writing—original draft preparation; Xinyu Huang: data curation, reviewing, and editing; Xiaojun Zhou: reviewing, editing, and methodology; Junfei Guo: software, formal analysis, and conceptualization; Xiaohu Yang: reviewing, editing, and data curation; Jinyue Yan: supervision, reviewing, and editing.

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Correspondence to Xiaohu Yang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Lu, L., Huang, X., Zhou, X. et al. High-performance formaldehyde prediction for indoor air quality assessment using time series deep learning. Build. Simul. 17, 415–429 (2024). https://doi.org/10.1007/s12273-023-1091-4

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  • DOI: https://doi.org/10.1007/s12273-023-1091-4

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