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Development of Sustainable Indoor Air Quality for Air-Conditioning System Using Smart Control Techniques

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Listed:
  • Tosin T. Oye
  • Naren Gupta
  • Keng Goh
  • Toyosi K. Oye

Abstract

Air-conditioning as a technical solution to protect inhabitants from excessive heat exposure creates the challenge of expanding indoor health effects. While air-conditioning has mostly been applied as an improvement to living conditions, health and environmental problems associated with its use frequently occurs. Therefore, this paper challenges and extends existing knowledge on sustainability related to the smart air-conditioning systems. The decrease of CO2 level in building requires an intelligent control system because energy utilisation has been legitimately connected with wellbeing and eventually to operational expenses. A building’s indoor environmental essential factors of comfort are IAQ, visual and thermal. Through an appropriate structured controller, the performance of indoor control system can be altogether improved. It merits creating innovative control techniques to optimise the indoor environment quality for air-conditioning system. The newly proposed backpropagation neural network was optimised using Matlab to control the CO2 level appropriately while carefully taking into account the performance of system controllers such as the stability, adaptability, speed response and overshoot. The controller of indoor environment was designed, and the proportional-integral-derivative control was utilised as a result of its suitability. The smart controllers were designed to regulate the parameters automatically to ensure the optimised control output. The indoor CO2 possesses an appropriate time constant and settling time of 2.1s and 27.3s, respectively. Therefore, utilising smart control techniques to exterminate various indoor health effects is expected to produce sustainable living conditions.

Suggested Citation

  • Tosin T. Oye & Naren Gupta & Keng Goh & Toyosi K. Oye, 2022. "Development of Sustainable Indoor Air Quality for Air-Conditioning System Using Smart Control Techniques," Environmental Management and Sustainable Development, Macrothink Institute, vol. 11(1), pages 1-37, December.
    • Handle: RePEc:mth:emsd88:v:11:y:2022:i:1:p:1-37
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    References listed on IDEAS

    as
    1. Chul-Ho Kim & Seung-Eon Lee & Kwang-Ho Lee & Kang-Soo Kim, 2019. "Detailed Comparison of the Operational Characteristics of Energy-Conserving HVAC Systems during the Cooling Season," Energies, MDPI, vol. 12(21), pages 1-29, October.
    2. Soteris A. Kalogirou, 2006. "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 1(3), pages 201-216, July.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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