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A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence

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  • Prince,
  • Hati, Ananda Shankar

Abstract

Energy utilization throughout the world is expanding at an alarming rate. This has just demonstrated its effect on the consumption of fuel resources and natural concerns (a drastic atmospheric deviation and debilitating of the O3 layer). Positively, this heightening propensity of energy inadequacy will get disturbed later on. Effectiveness upgrade activities are viewed as the necessary arrangement in decreasing energy usage and ultimately opposing the worldwide natural effects of the all-out world energy produced, ventilation system, particularly the axial fans, burn-through about 20%. Hence, the essential focus on worldwide energy policy-makers is to upgrade energy effectiveness in the ventilation system. According to the studies, striking energy investment funds can be refined by controlling the ventilation system speed utilizing VFDs. Therefore, studies and investigations center principally around VFD control strategies to improve the ventilation system effectiveness. This article gave state-of-the-artwork on the ventilation system and VSD that reduces the ventilation system’s overall energy consumption. Various energy efficiency strategies, selection of various components, and intelligent flow prediction techniques are represented in this article. Finally, the prospect encounters of AI-based models employed in the environmental area are discussed and proposed.

Suggested Citation

  • Prince, & Hati, Ananda Shankar, 2021. "A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    • Handle: RePEc:eee:rensus:v:146:y:2021:i:c:s1364032121004421
    DOI: 10.1016/j.rser.2021.111153
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    Cited by:

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    2. Sousa Santos, Vladimir & Cabello Eras, Juan J. & Cabello Ulloa, Mario J., 2024. "Evaluation of the energy saving potential in electric motors applying a load-based voltage control method," Energy, Elsevier, vol. 303(C).
    3. Marian Piwowarski & Damian Jakowski, 2023. "Areas of Fan Research—A Review of the Literature in Terms of Improving Operating Efficiency and Reducing Noise Emissions," Energies, MDPI, vol. 16(3), pages 1-28, January.
    4. Prince, & Hati, Ananda Shankar & Kumar, Prashant, 2023. "An adaptive neural fuzzy interface structure optimisation for prediction of energy consumption and airflow of a ventilation system," Applied Energy, Elsevier, vol. 337(C).

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