IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v173y2023ics1364032122008966.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032122008966
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2022.113015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Barone, Giovanni & Buonomano, Annamaria & Giuzio, Giovanni Francesco & Palombo, Adolfo, 2023. "Towards zero energy infrastructure buildings: optimal design of envelope and cooling system," Energy, Elsevier, vol. 279(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barone, Giovanni & Buonomano, Annamaria & Chang, Roma & Forzano, Cesare & Giuzio, Giovanni Francesco & Mondol, Jayanta & Palombo, Adolfo & Pugsley, Adrian & Smyth, Mervyn & Zacharopoulos, Aggelos, 2022. "Modelling and simulation of building integrated Concentrating Photovoltaic/Thermal Glazing (CoPVTG) systems: Comprehensive energy and economic analysis," Renewable Energy, Elsevier, vol. 193(C), pages 1121-1131.
    2. Athienitis, Andreas K. & Barone, Giovanni & Buonomano, Annamaria & Palombo, Adolfo, 2018. "Assessing active and passive effects of façade building integrated photovoltaics/thermal systems: Dynamic modelling and simulation," Applied Energy, Elsevier, vol. 209(C), pages 355-382.
    3. Buonomano, Annamaria & Guarino, Francesco, 2020. "The impact of thermophysical properties and hysteresis effects on the energy performance simulation of PCM wallboards: Experimental studies, modelling, and validation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 126(C).
    4. Barone, Giovanni & Buonomano, Annamaria & Forzano, Cesare & Giuzio, Giovanni Francesco & Palombo, Adolfo, 2020. "Passive and active performance assessment of building integrated hybrid solar photovoltaic/thermal collector prototypes: Energy, comfort, and economic analyses," Energy, Elsevier, vol. 209(C).
    5. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    6. Agathokleous, R. & Barone, G. & Buonomano, A. & Forzano, C. & Kalogirou, S.A. & Palombo, A., 2019. "Building façade integrated solar thermal collectors for air heating: experimentation, modelling and applications," Applied Energy, Elsevier, vol. 239(C), pages 658-679.
    7. Barone, G. & Buonomano, A. & Calise, F. & Forzano, C. & Palombo, A., 2019. "Building to vehicle to building concept toward a novel zero energy paradigm: Modelling and case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 625-648.
    8. Buonomano, Annamaria, 2020. "Building to Vehicle to Building concept: A comprehensive parametric and sensitivity analysis for decision making aims," Applied Energy, Elsevier, vol. 261(C).
    9. 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.
    10. Barone, Giovanni & Zacharopoulos, Aggelos & Buonomano, Annamaria & Forzano, Cesare & Giuzio, Giovanni Francesco & Mondol, Jayanta & Palombo, Adolfo & Pugsley, Adrian & Smyth, Mervyn, 2022. "Concentrating PhotoVoltaic glazing (CoPVG) system: Modelling and simulation of smart building façade," Energy, Elsevier, vol. 238(PB).
    11. Lee, Minjung & Ham, Jeonggyun & Lee, Jeong-Won & Cho, Honghyun, 2023. "Analysis of thermal comfort, energy consumption, and CO2 reduction of indoor space according to the type of local heating under winter rest conditions," Energy, Elsevier, vol. 268(C).
    12. López-Pérez, Luis Adrián & Flores-Prieto, José Jassón, 2023. "Adaptive thermal comfort approach to save energy in tropical climate educational building by artificial intelligence," Energy, Elsevier, vol. 263(PA).
    13. Amasyali, Kadir & El-Gohary, Nora M., 2021. "Real data-driven occupant-behavior optimization for reduced energy consumption and improved comfort," Applied Energy, Elsevier, vol. 302(C).
    14. Pisello, Anna Laura & Asdrubali, Francesco, 2014. "Human-based energy retrofits in residential buildings: A cost-effective alternative to traditional physical strategies," Applied Energy, Elsevier, vol. 133(C), pages 224-235.
    15. 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.
    16. Jarvinen, J. & Goldsworthy, M. & White, S. & Pudney, P. & Belusko, M. & Bruno, F., 2021. "Evaluating the utility of passive thermal storage as an energy storage system on the Australian energy market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    17. 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).
    18. Gao, Hao & Koch, Christian & Wu, Yupeng, 2019. "Building information modelling based building energy modelling: A review," Applied Energy, Elsevier, vol. 238(C), pages 320-343.
    19. Calise, F. & Cappiello, F. & D'Agostino, D. & Vicidomini, M., 2021. "Heat metering for residential buildings: A novel approach through dynamic simulations for the calculation of energy and economic savings," Energy, Elsevier, vol. 234(C).
    20. Francesca Marcello & Virginia Pilloni & Daniele Giusto, 2019. "Sensor-Based Early Activity Recognition Inside Buildings to Support Energy and Comfort Management Systems," Energies, MDPI, vol. 12(13), pages 1-18, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:173:y:2023:i:c:s1364032122008966. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.