IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i9p2551-d545990.html
   My bibliography  Save this article

A Substitutive Coefficients Network for the Modelling of Thermal Systems: A Mono-Zone Building Case Study

Author

Listed:
  • Lahoucine Ouhsaine

    (Department of Thermal and Energy Engineering, LERMAB, IUT Henri Poincaré, University of Lorraine, 54400 Longwy, France
    Energetic Laboratory, Faculty of Sciences, Abdelmalek Essaâdi University, Tétouan 93030, Morocco)

  • Mohammed El Ganaoui

    (Department of Thermal and Energy Engineering, LERMAB, IUT Henri Poincaré, University of Lorraine, 54400 Longwy, France)

  • Abdelaziz Mimet

    (Energetic Laboratory, Faculty of Sciences, Abdelmalek Essaâdi University, Tétouan 93030, Morocco)

  • Jean-Michel Nunzi

    (Department of Thermal and Energy Engineering, LERMAB, IUT Henri Poincaré, University of Lorraine, 54400 Longwy, France
    Department of Chemistry, Queen’s University, Kingston, ON K7L-3N6, Canada
    Department of Physics, Engineering Physics and Astronomy, Queen’s University, Kingston, ON K7L-3N6, Canada)

Abstract

A modelling approach based on the Substitutive Coefficients Network (SCN) is developed to predict the thermal behavior of a system in the dynamic state-space, without requiring knowledge of the thermal mass. The method can apply either to large- (building, combined solar systems, geothermal energy, and thermodynamic installations) or to small-scale systems (heat exchangers, electronic devices cooling systems, and Li-ion batteries). This current method is based on a dimensionless formulation of the simplified dynamic thermal balance model, using relaxation time as a key parameter to establish the model. The introduction of relaxation time reduces the parameters set as guidance coefficients. The parameters are finally expressed by a combination of global heat transfer coefficients related to each layer and/or sub-layer of the system. Advantages of the method are reliability, “non-destructibility”, i.e., it allows a reliable prediction of the thermal behavior which experimentally is inaccessible, and reducibility of the parameters size estimate. Additionally, the method is inexpensive in terms of computation memory. It is also easy to implement in practical numerical schemes. In this paper, the method leads to a simplified mathematical model that predicts the thermal behavior of a mono-zone eco-cottage building installed at Lorraine University (in Longwy, France) as a case study. Thermal performance of the building is estimated under the hourly weather conditions onsite, as obtained from the Meteonorm software. The thermal dynamics within hourly Typical Meteorological Year 2 (TMY2) Meteonorm data disturbances and the internal heating input state in the winter period were simulated with a simplified numerical discretization method. Results provide a general dynamic state of the different sub-components of the system, with limited design of the model parameters.

Suggested Citation

  • Lahoucine Ouhsaine & Mohammed El Ganaoui & Abdelaziz Mimet & Jean-Michel Nunzi, 2021. "A Substitutive Coefficients Network for the Modelling of Thermal Systems: A Mono-Zone Building Case Study," Energies, MDPI, vol. 14(9), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2551-:d:545990
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/9/2551/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/9/2551/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    2. Habibi Khalaj, Ali & Halgamuge, Saman K., 2017. "A Review on efficient thermal management of air- and liquid-cooled data centers: From chip to the cooling system," Applied Energy, Elsevier, vol. 205(C), pages 1165-1188.
    Full references (including those not matched with items on IDEAS)

    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. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    2. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    3. Lai, Yuan & Papadopoulos, Sokratis & Fuerst, Franz & Pivo, Gary & Sagi, Jacob & Kontokosta, Constantine E., 2022. "Building retrofit hurdle rates and risk aversion in energy efficiency investments," Applied Energy, Elsevier, vol. 306(PB).
    4. Zhijia Huang & Xiaofeng Chen & Kaiwen Wang & Binbin Zhou, 2022. "Air Conditioning Load Forecasting and Optimal Operation of Water Systems," Sustainability, MDPI, vol. 14(9), pages 1-12, April.
    5. Cheng Liu & Hang Yu, 2021. "Evaluation and Optimization of a Two-Phase Liquid-Immersion Cooling System for Data Centers," Energies, MDPI, vol. 14(5), pages 1-21, March.
    6. Stefano Converso & Paolo Civiero & Stefano Ciprigno & Ivana Veselinova & Saffa Riffat, 2023. "Toward a Fast but Reliable Energy Performance Evaluation Method for Existing Residential Building Stock," Energies, MDPI, vol. 16(9), pages 1-24, May.
    7. Fahlstedt, Oskar & Temeljotov-Salaj, Alenka & Lohne, Jardar & Bohne, Rolf André, 2022. "Holistic assessment of carbon abatement strategies in building refurbishment literature — A scoping review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    8. Yoon, Y. & Jung, S. & Im, P. & Salonvaara, M. & Bhandari, M. & Kunwar, N., 2023. "Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    9. Pedro Paulo Fernandes da Silva & Alberto Hernandez Neto & Ildo Luis Sauer, 2021. "Evaluation of Model Calibration Method for Simulation Performance of a Public Hospital in Brazil," Energies, MDPI, vol. 14(13), pages 1-20, June.
    10. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    11. Xin, Fei & Ma, Ting & Wang, Qiuwang, 2018. "Thermal performance analysis of flat heat pipe with graded mini-grooves wick," Applied Energy, Elsevier, vol. 228(C), pages 2129-2139.
    12. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2023. "Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models," Applied Energy, Elsevier, vol. 340(C).
    13. Cristina Ramos Cáceres & Suzanna Törnroth & Mattias Vesterlund & Andreas Johansson & Marcus Sandberg, 2022. "Data-Center Farming: Exploring the Potential of Industrial Symbiosis in a Subarctic Region," Sustainability, MDPI, vol. 14(5), pages 1-23, February.
    14. Ye, Guisen & Gao, Feng & Fang, Jingyang, 2022. "A mission-driven two-step virtual machine commitment for energy saving of modern data centers through UPS and server coordinated optimizations," Applied Energy, Elsevier, vol. 322(C).
    15. Xiaofei Huang & Junwei Yan & Xuan Zhou & Yixin Wu & Shichen Hu, 2023. "Cooling Technologies for Internet Data Center in China: Principle, Energy Efficiency, and Applications," Energies, MDPI, vol. 16(20), pages 1-31, October.
    16. Muhammad Ali & Krishneel Prakash & Carlos Macana & Ali Kashif Bashir & Alireza Jolfaei & Awais Bokhari & Jiří Jaromír Klemeš & Hemanshu Pota, 2022. "Modeling Residential Electricity Consumption from Public Demographic Data for Sustainable Cities," Energies, MDPI, vol. 15(6), pages 1-16, March.
    17. Dongsu Kim & Yongjun Lee & Kyungil Chin & Pedro J. Mago & Heejin Cho & Jian Zhang, 2023. "Implementation of a Long Short-Term Memory Transfer Learning (LSTM-TL)-Based Data-Driven Model for Building Energy Demand Forecasting," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    18. Jacek Fal & Omid Mahian & Gaweł Żyła, 2018. "Nanofluids in the Service of High Voltage Transformers: Breakdown Properties of Transformer Oils with Nanoparticles, a Review," Energies, MDPI, vol. 11(11), pages 1-46, October.
    19. Zhang, Yingbo & Shan, Kui & Li, Xiuming & Li, Hangxin & Wang, Shengwei, 2023. "Research and Technologies for next-generation high-temperature data centers – State-of-the-arts and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    20. Wang, Xinyue & Liu, Yang & Tian, Tong & Li, Ji, 2022. "Directly air-cooled compact looped heat pipe module for high power servers with extremely low power usage effectiveness," Applied Energy, Elsevier, vol. 319(C).

    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:gam:jeners:v:14:y:2021:i:9:p:2551-:d:545990. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.