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Development of a Novel Experimental Facility to Assess Heating Systems’ Behaviour in Buildings

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  • Wirich Freppel

    (Laboratory of Innovative Technologies (EA3899), University of Picardie Jules Verne, Avenue des Facultés—Le Bailly, CEDEX, 80025 Amiens, France
    Noirot, Groupe Muller, 8 Rue Ampère, CEDEX 02, 02000 Laon, France)

  • Geoffrey Promis

    (Laboratory of Innovative Technologies (EA3899), University of Picardie Jules Verne, Avenue des Facultés—Le Bailly, CEDEX, 80025 Amiens, France)

  • Anh Dung Tran Le

    (Laboratory of Innovative Technologies (EA3899), University of Picardie Jules Verne, Avenue des Facultés—Le Bailly, CEDEX, 80025 Amiens, France)

  • Omar Douzane

    (Laboratory of Innovative Technologies (EA3899), University of Picardie Jules Verne, Avenue des Facultés—Le Bailly, CEDEX, 80025 Amiens, France)

  • Thierry Langlet

    (Laboratory of Innovative Technologies (EA3899), University of Picardie Jules Verne, Avenue des Facultés—Le Bailly, CEDEX, 80025 Amiens, France)

Abstract

The building sector represents approximately 40% of the global energy consumption, of which 18 to 73% is represented by heating and ventilation. One focus of research for reducing energy consumption is to study the interaction between the heating system, the occupant’s behaviour, and the building’s thermal mass. For this purpose, a new experimental facility was developed. It consists of a real accommodation in which the thermal performance of the envelope, the heating system, the room’s layout, the weather conditions, and the occupant’s activity are variable parameters. A simulation model of the experimental facility, built in TRNSYS, was used to characterise the experimental facility. This article details the development of the experimental facility and then compares results for two different types of building inertia (low and high thermal masses). Results show the accuracy of the thermal inertia reproduction in the experimental facility and highlight the possibilities of improvements in the interaction between heating systems and building envelope efficiency.

Suggested Citation

  • Wirich Freppel & Geoffrey Promis & Anh Dung Tran Le & Omar Douzane & Thierry Langlet, 2022. "Development of a Novel Experimental Facility to Assess Heating Systems’ Behaviour in Buildings," Energies, MDPI, vol. 15(13), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4615-:d:846468
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    References listed on IDEAS

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    1. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
    2. Bond, Danielle E.M. & Clark, William W. & Kimber, Mark, 2013. "Configuring wall layers for improved insulation performance," Applied Energy, Elsevier, vol. 112(C), pages 235-245.
    3. Mazzeo, D. & Oliveti, G. & Arcuri, N., 2016. "Influence of internal and external boundary conditions on the decrement factor and time lag heat flux of building walls in steady periodic regime," Applied Energy, Elsevier, vol. 164(C), pages 509-531.
    4. Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
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