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Assessment of ANN Algorithms for the Concentration Prediction of Indoor Air Pollutants in Child Daycare Centers

Author

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  • Jeeheon Kim

    (Eco-System Research Center, Gachon University, Seongnam 13120, Korea)

  • Yongsug Hong

    (Division of Human-Architectural Engineering, Daejin University, 1007 Hoguk-Ro, Pocheon 11159, Korea)

  • Namchul Seong

    (Department of Architectural Engineering Kangwon National University, Samcheok-si 25913, Korea)

  • Daeung Danny Kim

    (Architectural Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

Abstract

As the time spent by people indoors continues to significantly increase, much attention has been paid to indoor air quality. While many IAQ studies have been conducted through field measurements, the use of data-driven techniques such as machine learning has been increasingly used for the prediction of indoor air pollutants. For the present study, the concentrations of indoor air pollutants such as CO 2 , PM 2.5 , and VOCs in child daycare centers were predicted by using an artificial neural network model with three different training algorithms including Levenberg–Marquardt, Bayesian regularization, and Broyden–Fletcher–Goldfarb–Shanno quasi-Newton methods. For training and validation, data of indoor pollutants measured in child daycare facilities over a 1-month period were used. The results showed all the models produced a good performance for the prediction of indoor pollutants compared with the measured data. Among the models, the prediction by the LM model met the acceptable criteria of ASHRAE guideline 14 under all conditions. It was observed that the prediction performance decreased as the number of hidden layers increased. Moreover, the prediction performance was differed by the type of indoor pollutant. This was caused by patterns observed in the measured data. Considering the outcomes of the study, better prediction results can be obtained through the selection of suitable prediction models for time series data as well as the adjustment of training algorithms.

Suggested Citation

  • Jeeheon Kim & Yongsug Hong & Namchul Seong & Daeung Danny Kim, 2022. "Assessment of ANN Algorithms for the Concentration Prediction of Indoor Air Pollutants in Child Daycare Centers," Energies, MDPI, vol. 15(7), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2654-:d:787436
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    References listed on IDEAS

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    1. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    2. Seyedmohammadreza Heibati & Wahid Maref & Hamed H. Saber, 2021. "Assessing the Energy, Indoor Air Quality, and Moisture Performance for a Three-Story Building Using an Integrated Model, Part Two: Integrating the Indoor Air Quality, Moisture, and Thermal Comfort," Energies, MDPI, vol. 14(16), pages 1-40, August.
    3. Volker Liermann & Sangmeng Li, 2021. "Methods of Machine Learning," Springer Books, in: Volker Liermann & Claus Stegmann (ed.), The Digital Journey of Banking and Insurance, Volume III, pages 225-238, Springer.
    4. Seyedmohammadreza Heibati & Wahid Maref & Hamed H. Saber, 2021. "Assessing the Energy, Indoor Air Quality, and Moisture Performance for a Three-Story Building Using an Integrated Model, Part Three: Development of Integrated Model and Applications," Energies, MDPI, vol. 14(18), pages 1-31, September.
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    Cited by:

    1. Jierui Dong & Nigel Goodman & Priyadarsini Rajagopalan, 2023. "A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools," IJERPH, MDPI, vol. 20(15), pages 1-18, July.
    2. Talib Dbouk & Dimitris Drikakis, 2022. "Natural Ventilation and Aerosol Particles Dispersion Indoors," Energies, MDPI, vol. 15(14), pages 1-11, July.

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