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On the Evolution and Application of the Thermal Network Method for Energy Assessments in Buildings

Author

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  • Ali Bagheri

    (‘Net-Zero Energy Efficiency on City Districts’ Unit, NZED, Research Institute for Energy, University of Mons, 7000 Mons, Belgium
    Thermal Engineering and Combustion Unit, University of Mons, 7000 Mons, Belgium)

  • Véronique Feldheim

    (Thermal Engineering and Combustion Unit, University of Mons, 7000 Mons, Belgium)

  • Christos S. Ioakimidis

    (‘Net-Zero Energy Efficiency on City Districts’ Unit, NZED, Research Institute for Energy, University of Mons, 7000 Mons, Belgium
    ERA Chair Holder.)

Abstract

This paper describes the evolution of the thermal network and its applications for making simplified thermal models of buildings by means of thermal resistances (R) and capacitances (C). In the literature, there are several modelling schemes for buildings. Here, we investigate the advantages, disadvantages, and improvements of thermal networks. The thermal network method has been used in different studies for calculating indoor air temperature and heating load, estimating model parameters, and studying building interactions with heating and cooling systems. This review paper conducts an investigation into the application, system identification, and structure of thermal networks compared to other tools. Within the framework of the thermal network method, we conclude with some new proposals for research in this field to expand the idea of the thermal network to other engineering and energy management fields.

Suggested Citation

  • Ali Bagheri & Véronique Feldheim & Christos S. Ioakimidis, 2018. "On the Evolution and Application of the Thermal Network Method for Energy Assessments in Buildings," Energies, MDPI, vol. 11(4), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:890-:d:140487
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    References listed on IDEAS

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    Cited by:

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    2. Teodora Melania Șoimoșan & Ligia Mihaela Moga & Gelu Danku & Aurica Căzilă & Daniela Lucia Manea, 2019. "Assessing the Energy Performance of Solar Thermal Energy for Heat Production in Urban Areas: A Case Study," Energies, MDPI, vol. 12(6), pages 1-19, March.
    3. Azadeh Sadeghi & Roohollah Younes Sinaki & William A. Young & Gary R. Weckman, 2020. "An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks," Energies, MDPI, vol. 13(3), pages 1-23, January.
    4. Abhinandana Boodi & Karim Beddiar & Yassine Amirat & Mohamed Benbouzid, 2022. "Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives," Energies, MDPI, vol. 15(4), pages 1-27, February.
    5. Ali Bagheri & Konstantinos N. Genikomsakis & Véronique Feldheim & Christos S. Ioakimidis, 2021. "Sensitivity Analysis of 4R3C Model Parameters with Respect to Structure and Geometric Characteristics of Buildings," Energies, MDPI, vol. 14(3), pages 1-20, January.
    6. Piotr Michalak, 2022. "Thermal Network Model for an Assessment of Summer Indoor Comfort in a Naturally Ventilated Residential Building," Energies, MDPI, vol. 15(10), pages 1-19, May.
    7. Marco Massano & Edoardo Patti & Enrico Macii & Andrea Acquaviva & Lorenzo Bottaccioli, 2020. "An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings," Energies, MDPI, vol. 13(8), pages 1-17, April.
    8. Bienvenido-Huertas, David & Moyano, Juan & Marín, David & Fresco-Contreras, Rafael, 2019. "Review of in situ methods for assessing the thermal transmittance of walls," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 356-371.
    9. Alejandra Aversa & Luis Ballestero & Miguel Chen Austin, 2022. "Highlighting the Probabilistic Behavior of Occupants’ Preferences in Energy Consumption by Integrating a Thermal Comfort Controller in a Tropical Climate," Sustainability, MDPI, vol. 14(15), pages 1-16, August.

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