A Fully Distributed Genetic Algorithm for Global Optimization of HVAC Systems

  • Shiqiang Wang
  • Jianchun XingEmail author
  • Juelong Li
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)


To solve the high labor and maintenance cost problems in actual engineering, a decentralized heating, ventilation, and air-conditioning (HVAC) system is configured following its physical layout. In a decentralized HVAC control system, each of the updated smart equipment can communicate with the adjacent nodes collaboratively to fulfill the load requirement. Furthermore, to achieve the global optimal operation of an HVAC system, a fully distributed constrained optimization is formulated. In this paper, a fully distributed genetic algorithm (GA) is developed to solve the proposed constrained optimization. The proposed method is confirmed to be effective to realize the global optimization of HVAC system through simulation study.


HVAC system Global optimization Self-Organizing Decentralized genetic algorithm 



This work is supported by the National Key Research and Development Project of China No. 2017YFC0704100 (entitled New Generation Intelligent Building Platform Techniques).


  1. 1.
    Fong, K.F., Hanby, V.I., Chow, T.T.: HVAC system optimization for energy management by evolutionary programming. Energy Build. 38(3), 220–231 (2006)CrossRefGoogle Scholar
  2. 2.
    Pérez-Lombard, L., Ortiz, J., Pout, C.: A review on buildings energy consumption information. Energy Build. 40(3), 394–398 (2014)CrossRefGoogle Scholar
  3. 3.
    Cai, J., Kim, D., Jaramillo, R., et al.: A general multi-agent control approach for building energy system optimization. Energy Build. 127, 337–351 (2016)CrossRefGoogle Scholar
  4. 4.
    Marini, D.: Optimization of HVAC systems for distributed generation as a function of different types of heat sources and climatic conditions. Appl. Energy 102(2), 813–826 (2013)CrossRefGoogle Scholar
  5. 5.
    Wang, S., Burnett, J.: Online adaptive control for optimizing variable-speed pumps of indirect water-cooled chilling systems. Appl. Therm. Eng. 21(11), 1083–1103 (2001)CrossRefGoogle Scholar
  6. 6.
    Fan, B., Jin, X., Du, Z.: Optimal control strategies for multi-chiller system based on probability density distribution of cooling load ratio. Energy Build. 43(10), 2813–2821 (2011)CrossRefGoogle Scholar
  7. 7.
    Lu, L., Cai, W., Chai, Y.S., et al.: Global optimization for overall HVAC systems—part I problem formulation and analysis. Energy Convers. Manag. 46(7), 999–1014 (2005)CrossRefGoogle Scholar
  8. 8.
    Lu, L., Cai, W., Soh, Y.C., et al.: Global optimization for overall HVAC systems—part II problem solution and simulations. Energy Convers. Manag. 46(7–8), 1015–1028 (2005)CrossRefGoogle Scholar
  9. 9.
    Sun, J., Reddy, A.: Optimal control of building HVAC&R systems using complete simulation-based sequential quadratic programming (CSB-SQP). Build. Environ. 40(5), 657–669 (2005)CrossRefGoogle Scholar
  10. 10.
    Ahn, B.C., Mitchell, J.W.: Optimal control development for chilled water plants using a quadratic representation. Energy Build. 33(4), 371–378 (2001)CrossRefGoogle Scholar
  11. 11.
    Dullinger, C., Struckl, W., Kozek, M., et al.: A general approach for mixed-integer predictive control of HVAC systems using MILP. Appl. Therm. Eng. (2017)Google Scholar
  12. 12.
    Seo, J., Ooka, R., Kim, J.T., et al.: Optimization of the HVAC system design to minimize primary energy demand. Energy Build. 76(2), 102–108 (2014)CrossRefGoogle Scholar
  13. 13.
    Kusiak, A., Li, M., Tang, F.: Modeling and optimization of HVAC energy consumption. Appl. Energy 87(10), 3092–3102 (2010)CrossRefGoogle Scholar
  14. 14.
    He, X., Zhang, Z., Kusiak, A.: Performance optimization of HVAC systems with computational intelligence algorithms. Energy Build. 81, 371–380 (2014)CrossRefGoogle Scholar
  15. 15.
    Attaran, S.M., Yusof, R., Selamat, H.: A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system. Appl. Therm. Eng. 99, 613–624 (2016)CrossRefGoogle Scholar
  16. 16.
    Ling, K.V., Dexter, A.L.: Expert control of air-conditioning plant. Autom. 30, 761–773 (1994)CrossRefGoogle Scholar
  17. 17.
    Ahmad, M.W., Mourshed, M., Yuce, B., et al.: Computational intelligence techniques for HVAC systems: a review. Build. Simul. 9(4), 359–398 (2016)CrossRefGoogle Scholar
  18. 18.
    Lu, L., Wenjian, C., Lihua, X., Shujiang, L., Yeng, C.S.: HVAC system optimization—in-building section. Energy Build. 37(1), 11–22 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Defense EngineeringArmy Engineering University of PLANanjingChina

Personalised recommendations