Building Simulation

, Volume 12, Issue 6, pp 1095–1106 | Cite as

Adaptive modeling for reliability in optimal control of complex HVAC systems

  • Hussain Syed AsadEmail author
  • Richard Kwok Kit Yuen
  • Jinfeng Liu
  • Junqi Wang
Research Article


The model-based real-time optimization (MRTO) of heating, ventilation, and air-conditioning (HVAC) systems is an efficient tool for improving energy efficiency and for effective operation. Model-based real-time optimization of HVAC systems needs to regularly optimize the set points for local-loop operation, taking into account the interactions between HVAC components with the help of system-performance models. MRTO relies on the accuracy of the performance model to provide reliability in decision making. In practice, due to high diversity in ambient conditions and load demands, system-model mismatches are difficult to avoid. This paper presents an adaptive, model-based, real-time optimization (AMRTO) approach for large-scale, complex HVAC systems, to counter any model mismatches by updating the performance model in real time with real-time measurements. Furthermore, to make this approach practically applicable and to keep the online training process computationally manageable, an empirical-physical model of HVAC system components was set up that is suitable for online training, and hybrid genetic algorithms (HGAs) method was used for faster, yet reliable, online training of the performance model. A case study was used to evaluate the performance of the proposed approach. The results demonstrated that the proposed AMRTO was able to provide energy saving approximately 8% and reduce the online computational burden by 99%.


heating ventilation and air-conditioning (HVAC) system model-based real-time optimization adaptive modeling hybrid genetic algorithms (HGAs) energy performance computation load 


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The authors would like to thank Dr. Gongsheng Huang at the City University of Hong Kong for his assistance. The work described in this paper was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 11209518).


  1. Ahmad MW, Mourshed M, Yuce B, Rezgui Y (2016). Computational intelligence techniques for HVAC systems: A review. Building Simulation, 9, 359–398.CrossRefGoogle Scholar
  2. Al-mulali U, Fereidouni HG, Lee JY, Sab CNBC (2013). Exploring the relationship between urbanization, energy consumption, and CO2 emission in MENA countries. Renewable and Sustainable Energy Reviews, 23, 107–112.CrossRefGoogle Scholar
  3. Asad HS, Yuen RKK, Huang G (2016). Degree of freedom based set-point reset scheme for HVAC real-time optimization. Energy and Buildings, 128, 349–359.CrossRefGoogle Scholar
  4. Asad HS, Yuen RKK, Huang G (2017a). Hybrid adaptive modeling to enhance robustness of real-time optimization. In: Proceedings of the 19th International Conference on Sustainable and Renewable Energy Engineering (ICSREE 2017), Boston, USA.Google Scholar
  5. Asad HS, Yuen RKK, Huang G (2017b). Multiplexed real-time optimization of HVAC systems with enhanced control stability. Applied Energy, 187, 640–651.CrossRefGoogle Scholar
  6. ASHRAE (2011). ASHRAE Handbook: HVAC Applications, SI edn. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  7. Blum DH, Arendt K, Rivalin L, Piette MA, Wetter M, Veje CT (2019). Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems. Applied Energy, 236, 410–425.CrossRefGoogle Scholar
  8. Bourdouxhe JP, Groodent M, LeBrun J (1998). Reference Guide for Dynamic Models of HVAC Equipment. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  9. Broyden CG (1970). The convergence of a class of double-rank minimization algorithms 1. General considerations. IMA Journal of Applied Mathematics, 6, 76–90.CrossRefGoogle Scholar
  10. Clark DR (1985). HVACSIM+ Building Systems and Equipment Simulation Program Reference Manual. Gaithersburg, MD, USA: National Bureau of Standards.CrossRefGoogle Scholar
  11. Du Z, Jin X, Fang X, Fan B (2016). A dual-benchmark based energy analysis method to evaluate control strategies for building HVAC systems. Applied Energy, 183, 700–714.CrossRefGoogle Scholar
  12. EIA (2006). International Energy Outlook 2006. Energy Information Administration. U.S. Department of Energy.Google Scholar
  13. EIA (2013). International Energy Outlook. Energy Information Administration. U.S. Department of Energy.Google Scholar
  14. Guo P, Wang X, Han Y (2010). The enhanced genetic algorithms for optimization design. In: Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics, Yantai, China.Google Scholar
  15. Hong T, Langevin J, Sun K (2018). Building simulation: Ten challenges. Building Simulation, 11, 871–898.CrossRefGoogle Scholar
  16. Kubota T, Watanabe R (2013). Model-based optimization of a multi-zone HVAC system for cooling. IFAC Proceedings Volumes, 46, 207–212.CrossRefGoogle Scholar
  17. Kusiak A, Xu GL (2012). Modeling and optimization of HVAC systems using a dynamic neural network. Energy, 42, 241–250.CrossRefGoogle Scholar
  18. Kusiak A, Xu G, Zhang Z (2014). Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method. Energy Conversion and Management, 85, 146–153.CrossRefGoogle Scholar
  19. Li L, Mu H, Gao W, Li M (2014). Optimization and analysis of CCHP system based on energy loads coupling of residential and office buildings. Applied Energy, 136, 206–216.CrossRefGoogle Scholar
  20. Li N, Cheung SC, Li X, Tu J (2017). Multi-objective optimization of HVAC system using NSPSO and Kriging algorithms-A case study. Building Simulation, 10, 769–781.CrossRefGoogle Scholar
  21. Liu Z, Song F, Jiang Z, Chen X, Guan X (2014). Optimization based integrated control of building HVAC system. Building Simulation, 7, 375–387.CrossRefGoogle Scholar
  22. Lu L, Cai W, Chai YS, Xie L (2005a). Global optimization for overall HVAC systems-Part I problem formulation and analysis. Energy Conversion and Management, 46, 999–1014.CrossRefGoogle Scholar
  23. Lu L, Cai W, Soh YC, Xie L (2005b). Global optimization for overall HVAC systems-Part II problem solution and simulations. Energy Conversion and Management, 46, 1015–1028.CrossRefGoogle Scholar
  24. Ma Z, Wang S (2011). Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm. Applied Energy, 88, 198–211.CrossRefGoogle Scholar
  25. Mui KWH, Chan WTD (2003). Adaptive comfort temperature model of air-conditioned building in Hong Kong. Building and Environment, 38, 837–852.CrossRefGoogle Scholar
  26. Nassif N, Moujaes S, Zaheeruddin M (2008). Self-tuning dynamic models of HVAC system components. Energy and Buildings, 40, 1709–1720.CrossRefGoogle Scholar
  27. Nassif N (2014). Modeling and optimization of HVAC systems using artificial neural network and genetic algorithm. Building Simulation, 7, 237–245.CrossRefGoogle Scholar
  28. Nishiguchi J, Konda T, Dazai R (2011). Adaptive optimization method for energy conservation in HVAC systems. ASHRAE Transactions, 117(1), 549–556.Google Scholar
  29. Pérez-Lombard L, Ortiz J, Pout C (2008). A review on buildings energy consumption information. Energy and Buildings, 40, 394–398.CrossRefGoogle Scholar
  30. Haupt RL, Haupt SE (2004). Practical Genetic Algorithms. Hoboken, NJ, USA: John Wiley & Sons.zbMATHGoogle Scholar
  31. Razmara M, Maasoumy M, Shahbakhti M, Robinett RD III (2015). Optimal exergy control of building HVAC system. Applied Energy, 156, 555–565.CrossRefGoogle Scholar
  32. Shanno DF (1970). Conditioning of quasi-Newton methods for function minimization. Mathematics of Computation, 24, 647–656.MathSciNetCrossRefGoogle Scholar
  33. Sun J, Reddy A (2005). Optimal control of building HVAC&R systems using complete simulation-based sequential quadratic programming (CSB-SQP). Building and Environment, 40, 657–669.CrossRefGoogle Scholar
  34. Sun Y, Huang G, Li Z, Wang S (2013). Multiplexed optimization for complex air conditioning systems. Building and Environment, 65, 99–108.CrossRefGoogle Scholar
  35. TRNSYS (2004). TRNSYS 16 Documentation. Available at
  36. Wang SW, Ma ZJ (2008). Supervisory and optimal control of building HVAC systems: A review. HVAC&R Research, 14, 3–32.CrossRefGoogle Scholar
  37. Wang S (2009). Intelligent Buildings and Building Automation. Abingdon, UK: Routledge.CrossRefGoogle Scholar
  38. Wang Q (2014). Effects of urbanisation on energy consumption in China. Energy Policy, 65, 332–339.CrossRefGoogle Scholar
  39. Wang J, Huang G, Sun Y, Liu X (2016). Event-driven optimization of complex HVAC systems. Energy and Buildings, 133, 79–87.CrossRefGoogle Scholar
  40. Wei X, Kusiak A, Li M, Tang F, Zeng Y (2015). Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance. Energy, 83, 294–306.CrossRefGoogle Scholar
  41. World Bank (2012). World Development Indicators. The World Bank, Washington, DC, USA. Available at Bank-2011.Google Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Hussain Syed Asad
    • 1
    Email author
  • Richard Kwok Kit Yuen
    • 1
  • Jinfeng Liu
    • 2
  • Junqi Wang
    • 3
  1. 1.Department of Architecture and Civil EngineeringCity University of Hong KongKowloon, Hong KongChina
  2. 2.Department of Chemical and Materials EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.School of Environmental Science and EngineeringSuzhou University of Science and TechnologySuzhouChina

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