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User-Centric BIM-Based Framework for HVAC Root-Cause Detection

Author

Listed:
  • Hamidreza Alavi

    (Group of Construction Research and Innovation (GRIC), Department of Project and Construction Engineering (DPCE), Universitat Politècnica de Catalunya (UPC), Colom, 11, Ed. TR5, 08222 Terrassa, Barcelona, Spain)

  • Nuria Forcada

    (Group of Construction Research and Innovation (GRIC), Department of Project and Construction Engineering (DPCE), Universitat Politècnica de Catalunya (UPC), Colom, 11, Ed. TR5, 08222 Terrassa, Barcelona, Spain)

Abstract

In the building operation phase, the Heating, Ventilation, and Air-Conditioning (HVAC) equipment are the main contributors to excessive energy consumption unless proper design and maintenance is carried out. Moreover, HVAC problems might have an impact on occupants’ discomfort in thermal comfort. Hence, the identification of the root cause of HVAC problems is imperative for facility managers to plan preventive and corrective maintenance actions. However, due to the complex interaction between various equipment and the lack of data integration among Facility Management (FM) systems, they fail to provide necessary information to identify the root cause of HVAC problems. Building Information Modelling (BIM) is a potential solution for maintenance activities to address the challenges of information reliability and interoperability. Therefore, this paper presents a novel conceptual model and user-centric framework to determine the causes of HVAC problems implemented in BIM for its visualization. CMMS and BMS data were integrated into BIM and utilized by the framework to analyze the root cause of HVAC problems. A case study in a university building was used to demonstrate the applicability of the approach. This framework assists the FM team to determine the most probable cause of an HVAC problem, reducing the time to detect equipment faults, and providing potential actions to solve them.

Suggested Citation

  • Hamidreza Alavi & Nuria Forcada, 2022. "User-Centric BIM-Based Framework for HVAC Root-Cause Detection," Energies, MDPI, vol. 15(10), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3674-:d:817676
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    References listed on IDEAS

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    1. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    2. Amir Rafati & Hamid Reza Shaker & Saman Ghahghahzadeh, 2022. "Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review," Energies, MDPI, vol. 15(1), pages 1-16, January.
    3. Chen, Jianli & Zhang, Liang & Li, Yanfei & Shi, Yifu & Gao, Xinghua & Hu, Yuqing, 2022. "A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    4. Claudio Giovanni Mattera & Hamid Reza Shaker & Muhyiddine Jradi, 2019. "Consensus-Based Method for Anomaly Detection in VAV Units," Energies, MDPI, vol. 12(3), pages 1-17, February.
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    Cited by:

    1. Muhammad Ali Musarat & Wesam Salah Alaloul & Lau Siew Cher & Abdul Hannan Qureshi & Aawag Mohsen Alawag & Abdullah O. Baarimah, 2023. "Applications of Building Information Modelling in the Operation and Maintenance Phase of Construction Projects: A Framework for the Malaysian Construction Industry," Sustainability, MDPI, vol. 15(6), pages 1-28, March.
    2. Rafaela Bortolini & Raul Rodrigues & Hamidreza Alavi & Luisa Felix Dalla Vecchia & Núria Forcada, 2022. "Digital Twins’ Applications for Building Energy Efficiency: A Review," Energies, MDPI, vol. 15(19), pages 1-17, September.

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