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Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance

Author

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  • Wei, Xiupeng
  • Kusiak, Andrew
  • Li, Mingyang
  • Tang, Fan
  • Zeng, Yaohui

Abstract

A data-driven approach to optimize the total energy consumption of the HVAC (heating, ventilation, and air conditioning) system in a typical office facility is presented. A multi-layer perceptron ensemble is selected to build the total energy model integrating three indoor air quality models, the facility temperature model, the facility relative humidity model, and the facility CO2 concentration model. To balance the energy consumption and the indoor air quality, a quad-objective optimization problem is constructed. The problem is solved with a modified particle swarm optimization algorithm producing control settings of supply air temperature and static pressure of the air handling unit. By assigning different weights to the objectives to the model, the generated control settings optimize HVAC system with the trade-off between the energy consumption and the facility thermal comfort. Significant energy savings can be obtained even with air quality constraint.

Suggested Citation

  • Wei, Xiupeng & Kusiak, Andrew & Li, Mingyang & Tang, Fan & Zeng, Yaohui, 2015. "Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance," Energy, Elsevier, vol. 83(C), pages 294-306.
    • Handle: RePEc:eee:energy:v:83:y:2015:i:c:p:294-306
    DOI: 10.1016/j.energy.2015.02.024
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    Cited by:

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    10. Verma, Anoop & Asadi, Ali & Yang, Kai & Tyagi, Satish, 2015. "A data-driven approach to identify households with plug-in electrical vehicles (PEVs)," Applied Energy, Elsevier, vol. 160(C), pages 71-79.
    11. Baghaee, H.R. & Mirsalim, M. & Gharehpetian, G.B. & Talebi, H.A., 2016. "Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system," Energy, Elsevier, vol. 115(P1), pages 1022-1041.
    12. Chen, Kang & Zhu, Xu & Anduv, Burkay & Jin, Xinqiao & Du, Zhimin, 2022. "Digital twins model and its updating method for heating, ventilation and air conditioning system using broad learning system algorithm," Energy, Elsevier, vol. 251(C).
    13. Liangwen Yan & Fengfeng Qian & Wei Li, 2018. "Research on Key Parameters Operation Range of Central Air Conditioning Based on Binary K-Means and Apriori Algorithm," Energies, MDPI, vol. 12(1), pages 1-13, December.
    14. Iivo Metsä-Eerola & Jukka Pulkkinen & Olli Niemitalo & Olli Koskela, 2022. "On Hourly Forecasting Heating Energy Consumption of HVAC with Recurrent Neural Networks," Energies, MDPI, vol. 15(14), pages 1-20, July.
    15. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Liu, Hongwu & Wang, Cheng, 2020. "An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control," Energy, Elsevier, vol. 199(C).
    16. Schmidt, Mischa & Åhlund, Christer, 2018. "Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 742-756.
    17. Lankeshwara, Gayan & Sharma, Rahul & Yan, Ruifeng & Saha, Tapan K., 2022. "A hierarchical control scheme for residential air-conditioning loads to provide real-time market services under uncertainties," Energy, Elsevier, vol. 250(C).
    18. Azar, Elie & Nikolopoulou, Christina & Papadopoulos, Sokratis, 2016. "Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling," Applied Energy, Elsevier, vol. 183(C), pages 926-937.
    19. Junqi Wang & Rundong Liu & Linfeng Zhang & Hussain Syed ASAD & Erlin Meng, 2019. "Triggering Optimal Control of Air Conditioning Systems by Event-Driven Mechanism: Comparing Direct and Indirect Approaches," Energies, MDPI, vol. 12(20), pages 1-20, October.
    20. Cheng, Fanyong & Cui, Can & Cai, Wenjian & Zhang, Xin & Ge, Yuan & Li, Bingxu, 2022. "A novel data-driven air balancing method with energy-saving constraint strategy to minimize the energy consumption of ventilation system," Energy, Elsevier, vol. 239(PB).
    21. Ferrara, Maria & Rolfo, Andrea & Prunotto, Federico & Fabrizio, Enrico, 2019. "EDeSSOpt – Energy Demand and Supply Simultaneous Optimization for cost-optimized design: Application to a multi-family building," Applied Energy, Elsevier, vol. 236(C), pages 1231-1248.
    22. Prince, & Hati, Ananda Shankar & Kumar, Prashant, 2023. "An adaptive neural fuzzy interface structure optimisation for prediction of energy consumption and airflow of a ventilation system," Applied Energy, Elsevier, vol. 337(C).
    23. Haosen Qin & Zhen Yu & Tailu Li & Xueliang Liu & Li Li, 2022. "Heating Control Strategy Based on Dynamic Programming for Building Energy Saving and Emission Reduction," IJERPH, MDPI, vol. 19(21), pages 1-27, October.
    24. Liang, Chao & Li, Xianting & Shao, Xiaoliang & Li, Baoming, 2020. "Direct relationship between the system cooling load and indoor heat gain in a non-uniform indoor environment," Energy, Elsevier, vol. 191(C).
    25. 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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