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FDD in Building Systems Based on Generalized Machine Learning Approaches

Author

Listed:
  • William Nelson

    (Department of Mechanical Engineering, Energy Systems Laboratory, Texas AM University, College Station, TX 77843, USA)

  • Charles Culp

    (Department of Architecture, Energy Systems Laboratory, Texas AM University, College Station, TX 77843, USA)

Abstract

Automated fault detection and diagnostics in building systems using machine learning (ML) can be applied to commercial buildings and can result in increased efficiency and savings. Using ML for FDD brings the benefit of advancing the analytics of a building. An automated process was developed to provide ML-based building analytics to building engineers and operators with minimal training. The process can be applied to buildings with a variety of configurations, which saves time and manual effort in a fault analysis. Classification analysis is used for fault detection and diagnostics. An ML analysis is defined which introduces advanced diagnostics with metrics to quantify a fault’s impact in the system and rank detected faults in order of impact severity. Explanations of the methodology used for the ML analysis include a description of the algorithms used. The analysis was applied to a building on the Texas A&M University campus where the results are shown to illustrate the performance of the process using measured data from a building.

Suggested Citation

  • William Nelson & Charles Culp, 2023. "FDD in Building Systems Based on Generalized Machine Learning Approaches," Energies, MDPI, vol. 16(4), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1637-:d:1059998
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    References listed on IDEAS

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    1. Lee, Won-Yong & House, John M. & Kyong, Nam-Ho, 2004. "Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks," Applied Energy, Elsevier, vol. 77(2), pages 153-170, February.
    2. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
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    Cited by:

    1. William Nelson & Christopher Dieckert, 2024. "Machine Learning-Based Automated Fault Detection and Diagnostics in Building Systems," Energies, MDPI, vol. 17(2), pages 1-23, January.

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