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

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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. 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).
    11. Li, Wenzhuo & Wang, Shengwei & Koo, Choongwan, 2021. "A real-time optimal control strategy for multi-zone VAV air-conditioning systems adopting a multi-agent based distributed optimization method," Applied Energy, Elsevier, vol. 287(C).
    12. Wangqi Xiong & Jiandong Wang, 2020. "Minimizing Power Consumption of an Experimental HVAC System Based on Parallel Grid Searching," Energies, MDPI, vol. 13(8), pages 1-18, April.
    13. Kim, Wonuk & Jeon, Yongseok & Kim, Yongchan, 2016. "Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method," Applied Energy, Elsevier, vol. 162(C), pages 666-674.
    14. Alperen Yayla & Kübra Sultan Świerczewska & Mahmut Kaya & Bahadır Karaca & Yusuf Arayici & Yunus Emre Ayözen & Onur Behzat Tokdemir, 2022. "Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings," Sustainability, MDPI, vol. 14(23), pages 1-29, December.
    15. Radhakrishnan, Nikitha & Su, Yang & Su, Rong & Poolla, Kameshwar, 2016. "Token based scheduling for energy management in building HVAC systems," Applied Energy, Elsevier, vol. 173(C), pages 67-79.
    16. Guiqiang Wang & Haiman Wang & Zhiqiang Kang & Guohui Feng, 2020. "Data-Driven Optimization for Capacity Control of Multiple Ground Source Heat Pump System in Heating Mode," Energies, MDPI, vol. 13(14), pages 1-15, July.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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).
    22. 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.
    23. 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).
    24. 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.
    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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