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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.

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References

  1. . A. Abdo-Allah,T. Iqbal, and K. Pope, ?Modeling, analysis, and state feedback control design of a multi-zone HVAC system,? Journal of Energy, vol. 2018, Article ID 4303580, 11 pages, 2018.
     Google Scholar
  2. . Abel, Enno, Per-Erik Nilsson, Lars Ekberg, Per Fahl?n, Lennart Jagemar, Roger Clark, Ole Fanger et al. ?Achieving the desired indoor climate-energy efficiency aspects of system design,? Studentlitteratur, 2003.
     Google Scholar
  3. . Awbi H. Ventilation of buildings, Spon Press; 2003.
     Google Scholar
  4. . Goodfellow, Howard D. Industrial ventilation design guidebook Elsevier, 2001.
     Google Scholar
  5. . American Society of Heating Refrigerating and Air-Conditioning Engineers, ASHRAE handbook, fundamentals, 2009.
     Google Scholar
  6. . S. Huang and R. M. Nelson, ?Rule development and adjustment strategies of a fuzzy logic controller for an hvac system: part one analysis,? ASHRAE Transactions-American Society of Heating Refrigerating Air Conditioning Engine, vol. 100, no. 1, pp. 841?850, 1994.
     Google Scholar
  7. . T. Tobi and T. Hanafusa, ?A practical application of fuzzy control for an air-conditioning system,? International Journal of Approximate Reasoning, vol. 5, no. 3, pp. 331?348, 1991.
     Google Scholar
  8. . J. Liang and R. Du, ?Thermal comfort control based on neural network for HVAC application,? in Proceedings of the IEEE Conference on Control Applications (CCA ?05), pp. 819?824, Toronto, Canada, 2005.
     Google Scholar
  9. . A. I. Dounis, M. J. Santamouris, and C. C. Lefas, ?Implementation of artificial intelligence techniques in thermal comfort control for passive solar buildings,? Energy Conversion and Management, vol. 33, no. 3, pp. 175?182, 1992.
     Google Scholar
  10. . C. V. Altrock, H.-O. Arend, B. Krause, C. Steffens, and E. Behrens-R?ommler, ?Adaptive fuzzy control applied to home heating system,? Fuzzy Sets and Systems, vol. 61, no. 1, pp. 29?35, 1994.
     Google Scholar
  11. . M. Trobec Lah, B. Zupan?ci?c, J. Peternelj, and A. Krainer, ?Daylight illuminance control with fuzzy logic,? Solar Energy, vol. 80, no. 3, pp. 307?321, 2006.
     Google Scholar
  12. . M. T. Lah, B. Zupan?ci?c, and A. Krainer, ?Fuzzy control for the illumination and temperature comfort in a test chamber,? Building and Environment, vol. 40, no. 12, pp. 1626?1637, 2005.
     Google Scholar
  13. . Honeywell. 1989. Engineering Manual of Automatic Control for Commercial Buildings: Heating, Ventilating, Air Conditioning. Minneapolis, MN: Honeywell Plaze.
     Google Scholar
  14. . Levenhagen, J.I., and D.H. Spethmann. 1993. HVAC Controls and Systems. New York. McGraw-Hill, Inc.
     Google Scholar
  15. . Wang, S.W., and X.Q. Jin. 2000. ?Model-based optimal control of VAV air-conditioning system using genetic algorithm,? Building and Environment 35(6):471?87.
     Google Scholar
  16. . Zaheer-uddin, M., and G.R. Zheng. 2000. ?Optimal control of time-scheduled heating, ventilating and air conditioning processes in building,? Energy Conversion and Management 41(1):49?60.
     Google Scholar
  17. . Hordeski, M.F. 2001. HVAC Control in the New Millennium. Lilburn, GA: The Fairmont Press, Inc.
     Google Scholar
  18. . Haines, R.W., and D.C. Hittle. 2003. Control Systems for Heating, Ventilating and Air Conditioning (Sixth Edition). Boston: Kluwer Academic Publishers.
     Google Scholar
  19. . Nassif, N., S. Kajl, and R. Sabourin. 2005. ?Optimization of HVAC control system strategy using two-objective genetic algorithm,? HVAC&R Research 11(3):459?86.
     Google Scholar
  20. . Wang, S.W. 2006. Editorial: ?Enhancing the applications of building automation systems for better building energy and environmental performance,? HVAC&R Research 12(2):197?99.
     Google Scholar
  21. . Wang, Shengwei, and Zhenjun Ma. ?Supervisory and optimal control of building HVAC systems: A review,? HVAC&R Research 14, no. 1 (2008): 3-32.
     Google Scholar
  22. . Kanagaraj, N., P. Sivashanmugam, and S. Paramasivam. ?A fuzzy logic based supervisory hierarchical control scheme for real time pressure control,? International Journal of Automation and Computing 6, no. 1 (2009): 88-96.
     Google Scholar
  23. . Soyguder, Servet, Mehmet Karakose, and Hasan Alli. ?Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system,? Expert systems with applications 36, no. 3 (2009): 4566-4573.
     Google Scholar
  24. . Shepherd, A. B., and W. J. Batty. ?Fuzzy control strategies to provide cost and energy efficient high-quality indoor environments in buildings with high occupant densities,? Building Services Engineering Research and Technology 24, no. 1 (2003): 35-45.
     Google Scholar
  25. . Lianzhong, L., and M. Zaheeruddin. ?Hybrid fuzzy logic control strategies for hot water district heating systems,? Building Services Engineering Research and Technology 28, no. 1 (2007): 35-53.
     Google Scholar
  26. . Hussain, Sajid, and Hossam A. Gabbar. ?A multi?objective evolutionary optimization of fuzzy controller for energy conservation in air conditioning systems,? International Journal of Energy Research 38, no. 7 (2014): 847-859.
     Google Scholar
  27. . Lygouras, John N., P. N. Botsaris, J. Vourvoulakis, and Vassilis Kodogiannis. ?Fuzzy logic controller implementation for a solar air-conditioning system,? Applied Energy 84, no. 12 (2007): 1305-1318.
     Google Scholar
  28. . A. Abdo-Allah, T. Iqbal, andK. Pope, ?Modeling and analysis of an HVAC system for the S.J. Carew Building at Memorial University,? in Proceedings of the 30th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE ?17), pp. 1?4, IEEE, Windsor, Canada, May 2017
     Google Scholar
  29. . Abdo-Allah, Almahdi, Tariq Iqbal, and Kevin Pope. ?Modeling, Analysis, and Design of a Fuzzy Logic Controller for an AHU in the SJ Carew Building at Memorial University,? Journal of Energy 2018 (2018).
     Google Scholar
  30. . Ljung, L. System identification Theory for the user, Second education, Prentice Hall PTR, (2006).
     Google Scholar
  31. . Afroz, Zakia, G. M. Shafiullah, Tania Urmee, and Gary Higgins. ?Modeling techniques used in building HVAC control systems: A review,? Renewable and Sustainable Energy Reviews 83 (2018): 64-84.
     Google Scholar
  32. . A. I. Dounis, M. J. Santamouris, C. C. Lefas, and A. Argiriou, ?Design of a fuzzy set environment comfort system,? Energy and Buildings, vol. 22, no. 1, pp. 81?87, 1995.
     Google Scholar
  33. . A. I. Dounis and D. E.Manolakis, ?Design of a fuzzy system for living space thermal-comfort regulation,? Applied Energy, vol. 69, no. 2, pp. 119?144, 2001.
     Google Scholar
  34. . A. I. Dounis and C. Caraiscos, ?Advanced control systems engineering for energy and comfort management in a building environment?a review,? Renewable & Sustainable Energy Reviews, vol. 13, no. 6-7, pp. 1246?1261, 2009.
     Google Scholar
  35. . Y. Bai and D. Wang, ?Fundamentals of fuzzy logic control?fuzzy sets, fuzzy rules and defuzzifications,? in Advances in Industrial Control, pp. 17?36, Springer, London, UK, 2006.
     Google Scholar
  36. . M. W. Khan, M. A. Choudhry, and M. Zeeshan, ?Multivariable adaptive Fuzzy logic controller design based on genetic algorithm applied to HVAC systems,? in Proceedings of the 3rd IEEE International Conference on Computer, Control and Communication (IC4 ?13), pp. 1?6, September 2013.
     Google Scholar
  37. . Khan, Muhammad Waqas, Mohammad Ahmad Choudhry, and Muhammad Zeeshan. ?An efficient design of genetic algorithm based adaptive fuzzy logic controller for multivariable control of hvac systems,? In 2013 5th Computer Science and Electronic Engineering Conference (CEEC), pp. 1-6. IEEE, 2013.
     Google Scholar
  38. . Venayagamoorthy, Ganesh K., and Sheetal Doctor. ?Navigation of mobile sensors using PSO and embedded PSO in a fuzzy logic controller,? In Conference Record of the 2004 IEEE Industry Applications Conference, 2004. 39th IAS Annual Meeting., vol. 2, pp. 1200-1206. IEEE, 2004.
     Google Scholar