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Indoor Air Quality Analysis Using Recurrent Neural Networks: A Case Study of Environmental Variables

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Listed:
  • Carlos A. Reyes Pérez

    (Grupo de Modelización Interdisciplinar, InterTech, Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain)

  • Miguel E. Iglesias Martínez

    (Grupo de Modelización Interdisciplinar, InterTech, Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain
    Grupo de Ingeniería Física, Escuela de Ingeniería Aeronáutica y del Espacio, Universidad de Vigo, Edif. Manuel Martínez Risco, Campus de As Lagoas, 32004 Ourense, Spain)

  • Jose Guerra-Carmenate

    (Grupo de Modelización Interdisciplinar, InterTech, Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain)

  • Humberto Michinel Álvarez

    (Grupo de Ingeniería Física, Escuela de Ingeniería Aeronáutica y del Espacio, Universidad de Vigo, Edif. Manuel Martínez Risco, Campus de As Lagoas, 32004 Ourense, Spain)

  • Eduardo Balvis

    (Departamento de Ingeniería de Sistemas y Automática, Escuela Superior de Ingeniería Informática, Universidade de Vigo, Edificio Politécnico s/n, 32004 Ourense, Spain)

  • Fernando Giménez Palomares

    (Grupo de Modelización Interdisciplinar, InterTech, Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain)

  • Pedro Fernández de Córdoba

    (Grupo de Modelización Interdisciplinar, InterTech, Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain)

Abstract

In the pursuit of energy efficiency and reduced environmental impact, adequate ventilation in enclosed spaces is essential. This study presents a hybrid neural network model designed for monitoring and prediction of environmental variables. The system comprises two phases: An IoT hardware–software platform for data acquisition and decision-making and a hybrid model combining short-term memory and convolutional recurrent structures. The results are promising and hold potential for integration into parallel processing AI architectures.

Suggested Citation

  • Carlos A. Reyes Pérez & Miguel E. Iglesias Martínez & Jose Guerra-Carmenate & Humberto Michinel Álvarez & Eduardo Balvis & Fernando Giménez Palomares & Pedro Fernández de Córdoba, 2023. "Indoor Air Quality Analysis Using Recurrent Neural Networks: A Case Study of Environmental Variables," Mathematics, MDPI, vol. 11(24), pages 1-17, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:24:p:4872-:d:1294081
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    References listed on IDEAS

    as
    1. Alessandra Cincinelli & Tania Martellini, 2017. "Indoor Air Quality and Health," IJERPH, MDPI, vol. 14(11), pages 1-5, October.
    2. Stuart Batterman, 2017. "Review and Extension of CO 2 -Based Methods to Determine Ventilation Rates with Application to School Classrooms," IJERPH, MDPI, vol. 14(2), pages 1-22, February.
    3. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
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