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Prediction of CO 2 in Public Buildings

Author

Listed:
  • Ekaterina Dudkina

    (Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, Largo Lucio Lazzarino, 2, 56122 Pisa, Italy
    These authors contributed equally to this work.)

  • Emanuele Crisostomi

    (Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, Largo Lucio Lazzarino, 2, 56122 Pisa, Italy
    These authors contributed equally to this work.)

  • Alessandro Franco

    (Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, Largo Lucio Lazzarino, 2, 56122 Pisa, Italy
    These authors contributed equally to this work.)

Abstract

Heritage from the COVID-19 period (in terms of massive utilization of mechanical ventilation systems), global warming, and increasing electricity prices are new challenging factors in building energy management, and are hindering the desired path towards improved energy efficiency and reduced building consumption. The solution to improve the smartness of today’s building and automation control systems is to equip them with increased intelligence to take prompt and appropriate actions to avoid unnecessary energy consumption, while maintaining a desired level of air quality. In this manuscript, we evaluate the ability of machine-learning-based algorithms to predict CO 2 levels, which are classic indicators used to evaluate air quality. We show that these algorithms provide accurate forecasts (more accurate in particular than those provided by physics-based models). These forecasts could be conveniently embedded in control systems. Our findings are validated using real data measured in university classrooms during teaching activities.

Suggested Citation

  • Ekaterina Dudkina & Emanuele Crisostomi & Alessandro Franco, 2023. "Prediction of CO 2 in Public Buildings," Energies, MDPI, vol. 16(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7582-:d:1280098
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    References listed on IDEAS

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    1. Li, Chunxiao & Cui, Can & Li, Ming, 2023. "A proactive 2-stage indoor CO2-based demand-controlled ventilation method considering control performance and energy efficiency," Applied Energy, Elsevier, vol. 329(C).
    2. Beibei Ren, 2020. "The use of machine translation algorithm based on residual and LSTM neural network in translation teaching," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-16, November.
    Full references (including those not matched with items on IDEAS)

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