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A new thermal comfort model based on physiological parameters for the smart design and control of energy-efficient HVAC systems

Author

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  • Barone, G.
  • Buonomano, A.
  • Forzano, C.
  • Giuzio, G.F.
  • Palombo, A.
  • Russo, G.

Abstract

Indoor thermal comfort represents a key aspect of building design. The reference standards do not consider the thermal adaptability of the human body, and HVAC system control strategies are based on a steady-state assumption that returns an incorrect estimate of occupants' thermal demand with a consequential misleading of the building energy consumption and of system sizing. To overcome these issues, a physiological thermal comfort model for the human body thermal behaviour evaluation is developed in MatLab environment for assessing the dynamic variation of the physiological parameters and for characterizing the occupants’ thermal sensation. Finally, the developed human body multi-node model is implemented in a building energy simulation tool (called DETECt 2.4) to perform three proposed strategies for the dynamic control of the building thermo-hygrometric parameters of the building and of the corresponding heating and cooling demands. These strategies provide an hourly regulation of relative humidity and air temperature by means of a two-step optimization that maximizes the thermal comfort of the occupants and minimizes energy consumption. To show the potentiality of the developed model, a suitable case study consisting of an office space is considered. Here, space heating and cooling demands obtained by applying the novel developed model are compared to those obtained through standard set-point values of air temperature and relative humidity (20 °C, 45% for heating needs, and 26 °C, 50% for cooling ones). By the comparison, between the proposed model – which considers the optimal hourly set point assessing the occupant in thermal evolution – and the reference ones, interesting results are obtained. Generally, a higher consumption is achieved by considering the proposed comfort strategies (from 2 to 16%), representing the price to be paid to maximize the comfort of the occupants and to annul the 3650 h of discomfort of the reference case.

Suggested Citation

  • Barone, G. & Buonomano, A. & Forzano, C. & Giuzio, G.F. & Palombo, A. & Russo, G., 2023. "A new thermal comfort model based on physiological parameters for the smart design and control of energy-efficient HVAC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:rensus:v:173:y:2023:i:c:s1364032122008966
    DOI: 10.1016/j.rser.2022.113015
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    References listed on IDEAS

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    1. Turhan, Cihan & Simani, Silvio & Gokcen Akkurt, Gulden, 2021. "Development of a personalized thermal comfort driven controller for HVAC systems," Energy, Elsevier, vol. 237(C).
    2. Buonomano, Annamaria & Montanaro, Umberto & Palombo, Adolfo & Santini, Stefania, 2016. "Dynamic building energy performance analysis: A new adaptive control strategy for stringent thermohygrometric indoor air requirements," Applied Energy, Elsevier, vol. 163(C), pages 361-386.
    3. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    4. Annamaria Buonomano, 2016. "Code-to-Code Validation and Application of a Dynamic Simulation Tool for the Building Energy Performance Analysis," Energies, MDPI, vol. 9(4), pages 1-29, April.
    5. Vassiliades, C. & Barone, G. & Buonomano, A. & Forzano, C. & Giuzio, G.F. & Palombo, A., 2022. "Assessment of an innovative plug and play PV/T system integrated in a prefabricated house unit: Active and passive behaviour and life cycle cost analysis," Renewable Energy, Elsevier, vol. 186(C), pages 845-863.
    6. Jung, Wooyoung & Jazizadeh, Farrokh, 2019. "Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions," Applied Energy, Elsevier, vol. 239(C), pages 1471-1508.
    7. Giovanni Barone & Annamaria Buonomano & Cesare Forzano & Adolfo Palombo, 2019. "Building Energy Performance Analysis: An Experimental Validation of an In-House Dynamic Simulation Tool through a Real Test Room," Energies, MDPI, vol. 12(21), pages 1-39, October.
    8. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    9. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    10. Jiang, Lai & Yao, Runming & Liu, Kecheng & McCrindle, Rachel, 2017. "An Epistemic-Deontic-Axiologic (EDA) agent-based energy management system in office buildings," Applied Energy, Elsevier, vol. 205(C), pages 440-452.
    11. Buonomano, Annamaria & Palombo, Adolfo, 2014. "Building energy performance analysis by an in-house developed dynamic simulation code: An investigation for different case studies," Applied Energy, Elsevier, vol. 113(C), pages 788-807.
    12. Soteris A. Kalogirou, 2006. "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 1(3), pages 201-216, July.
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    2. Couraud, Benoit & Andoni, Merlinda & Robu, Valentin & Norbu, Sonam & Chen, Si & Flynn, David, 2023. "Responsive FLEXibility: A smart local energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).

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