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Towards an Interoperable Approach for Modelling and Managing Smart Building Data: The Case of the CESI Smart Building Demonstrator

Autor(en):




Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Buildings, , n. 3, v. 12
Seite(n): 362
DOI: 10.3390/buildings12030362
Abstrakt:

Buildings have a significant impact on energy consumption and carbon emissions. Smart buildings are deemed to play a crucial role in improving the energy performance of buildings and cities. Managing a smart building requires the modelling of data concerning smart systems and components. While there is a significant amount of research on optimising building energy using the smart building concept, there is a dearth of studies investigating the modelling and management of smart systems’ data, which is the starting point for establishing the necessary digital environment for representing a smart building. This study aimed to develop and test a solution for modelling and managing smart building information using an industry foundation classes (IFCs)-based BIM process. A conceptual model expressed in the SysML language was proposed to define a smart building. Five BIM approaches were identified as potential ‘prototypes’ for representing and exchanging smart building information. The fidelity of each approach is checked through a BIM-based validation process using an open-source visualisation platform. The different prototypes were also assessed using a multi-criteria comparison method to identify the preferred approach for modelling and managing smart building information. The preferred approach was prototyped and tested in a use case focused on building energy consumption monitoring to evaluate its ability to manage and visualise the smart building data. The use case was applied in a real case study using a full-scale demonstrator, namely, the ‘Nanterre 3’ (N3) smart building located at the CESI campus in Paris-Nanterre. The findings demonstrated that an open BIM format in the form of IFCs could achieve adequate modelling of smart building data without information loss. Future extensions of the proposed approach were finally outlined.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
Lizenz:

Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

  • Über diese
    Datenseite
  • Reference-ID
    10661267
  • Veröffentlicht am:
    23.03.2022
  • Geändert am:
    01.06.2022
 
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