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Assessing barriers and research challenges for automated fault detection and diagnosis technology for small commercial buildings in the United States

Author

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  • Frank, Stephen
  • Jin, Xin
  • Studer, Daniel
  • Farthing, Amanda

Abstract

Commercial buildings often experience faults that waste energy, decrease occupant comfort, and increase operating costs. For medium and larger commercial buildings (buildings with more than approximately 1000 m2 [approximately 10,000 ft2] of floor area), studies have shown that automated fault detection and diagnosis (AFDD) tools can help building owners and operators identify and correct faults, improving building performance and producing up to 10% energy savings. However, the existing state of the art in AFDD tools and algorithms poorly serves the needs of commercial buildings less than approximately 1000 m2 (approximately 10,000 ft2). Using the United States market and building stock as a case study, this article characterizes AFDD needs for small commercial buildings, surveys the types of AFDD tools presently available in the market, identifies gaps and barriers to widespread adoption of AFDD technology in small commercial buildings, and makes recommendations for the future research and development of small buildings AFDD technology.

Suggested Citation

  • Frank, Stephen & Jin, Xin & Studer, Daniel & Farthing, Amanda, 2018. "Assessing barriers and research challenges for automated fault detection and diagnosis technology for small commercial buildings in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 489-499.
    • Handle: RePEc:eee:rensus:v:98:y:2018:i:c:p:489-499
    DOI: 10.1016/j.rser.2018.08.046
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    Cited by:

    1. Li, Guannan & Chen, Liang & Liu, Jiangyan & Fang, Xi, 2023. "Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis," Energy, Elsevier, vol. 263(PD).

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