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Supervisor Fuzzy Logic Controller for HVAC System of S.J Carew Building at Memorial University

Author

Listed:
  • Almahdi Abdo-Allah
  • M Tariq Iqbal
  • Kevin Pope

Abstract

One of the most important characteristics contributing to the thermal management efficiency of commercial, industrial, institutional or home environments is the optimal functioning of HVAC (heating, ventilation, air conditioning) systems. In addition to using supervisor controllers for balancing comfort level in a building, the majority of today’s HVACs employ nonlinear time variance controllers when dealing with a variety of disturbances. This paper investigates both current and potential HVAC systems at Memorial University’s S. J. Carew building, St. John’s, Newfoundland. The study investigates the viability of algorithm-based supervisor fuzzy logic controllers (SFLC) for the control of the building’s four air-handling units (AHUs) used to manage the interior environment. Along with temperature, the SFLCs also control the AHUs’ fan speeds and CO2 concentrations modifying hot water and air flow rates. This work presents models of damper positions, fan speeds and globe valves that have been built in accordance with current rates of air and hot water flow in the S. J. Carew building. Based on these specifications, a novel method of SFLC adaptation using fuzzy rules has been devised. The novel system aims to better balance the performance level of the Carew building’s HVAC system on a floor-by-floor basis. The overall results indicate better overall thermal comfort levels and enhanced cost-effectiveness when using the SFLC redesign.

Suggested Citation

  • Almahdi Abdo-Allah & M Tariq Iqbal & Kevin Pope, 2019. "Supervisor Fuzzy Logic Controller for HVAC System of S.J Carew Building at Memorial University," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 3(4), June.
  • Handle: RePEc:epw:ejece0:v:3:y:2019:i:4:id:19092
    DOI: 10.24018/ejece.2019.3.4.92
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    References listed on IDEAS

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    1. Dounis, A. I. & Manolakis, D. E., 2001. "Design of a fuzzy system for living space thermal-comfort regulation," Applied Energy, Elsevier, vol. 69(2), pages 119-144, June.
    2. Dounis, A.I. & Caraiscos, C., 2009. "Advanced control systems engineering for energy and comfort management in a building environment--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1246-1261, August.
    3. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
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