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Multi-objective optimization of HVAC system with an evolutionary computation algorithm

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  • Kusiak, Andrew
  • Tang, Fan
  • Xu, Guanglin

Abstract

A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables — supply air temperature and supply air duct static pressure set points — are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system.

Suggested Citation

  • Kusiak, Andrew & Tang, Fan & Xu, Guanglin, 2011. "Multi-objective optimization of HVAC system with an evolutionary computation algorithm," Energy, Elsevier, vol. 36(5), pages 2440-2449.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:5:p:2440-2449
    DOI: 10.1016/j.energy.2011.01.030
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    References listed on IDEAS

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    1. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    2. Ke, Yu-Pei & Mumma, Stanley A., 1997. "Optimized supply-air temperature (SAT) in variable-air-volume (VAV) systems," Energy, Elsevier, vol. 22(6), pages 601-614.
    3. Yu, F.W. & Chan, K.T., 2005. "Experimental determination of the energy efficiency of an air-cooled chiller under part load conditions," Energy, Elsevier, vol. 30(10), pages 1747-1758.
    4. Kusiak, Andrew & Li, Mingyang, 2010. "Cooling output optimization of an air handling unit," Applied Energy, Elsevier, vol. 87(3), pages 901-909, March.
    5. Mathews, E. H. & Arndt, D. C. & Piani, C. B. & van Heerden, E., 2000. "Developing cost efficient control strategies to ensure optimal energy use and sufficient indoor comfort," Applied Energy, Elsevier, vol. 66(2), pages 135-159, June.
    6. Zheng, G.R. & Zaheer-Uddin, M., 1996. "Optimization of thermal processes in a variable air volume HVAC system," Energy, Elsevier, vol. 21(5), pages 407-420.
    7. Zaheer-Uddin, M., 1994. "Intelligent control strategies for HVAC processes in buildings," Energy, Elsevier, vol. 19(1), pages 67-79.
    8. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    9. Tashtoush, Bourhan & Molhim, M. & Al-Rousan, M., 2005. "Dynamic model of an HVAC system for control analysis," Energy, Elsevier, vol. 30(10), pages 1729-1745.
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