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Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings

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  • Zhang, Liang
  • Leach, Matt
  • Chen, Jianli
  • Hu, Yuqing

Abstract

Data-driven building fault detection and diagnostics (FDD) is heavily dependent on sensors. However, common sensors from Building Automation Systems are not optimized to maximize accuracy in FDD. Installing additional sensors that provide more detailed building system information is key to maximizing the performance of FDD solutions. In this paper, we present a sensor cost analysis workflow to quantify the economic implications of installing new sensors for FDD using the concept of sensor threshold marginal cost (STMC). STMC does not represent actual sensor cost. Rather, it represents a target cost based on the economic benefit that would be realized through improved FDD performance and one or more specified economic criteria. We calculate STMCs for multiple possible fault types and use fault prevalence information to aggregate STMCs into a single dollar value to determine the cost-effectiveness of a potential sensor investment. We conducted a case study using Oak Ridge National Laboratory's Flexible Research Platform (FRP) test facility as a reference. The case study demonstrates the feasibility of the analysis and highlights the key cost considerations in sensor selection for FDD. The results also indicate that identifying and installing the few key sensor(s) is critical to cost-effectively improve FDD performance.

Suggested Citation

  • Zhang, Liang & Leach, Matt & Chen, Jianli & Hu, Yuqing, 2023. "Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings," Energy, Elsevier, vol. 263(PB).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pb:s036054422202463x
    DOI: 10.1016/j.energy.2022.125577
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    References listed on IDEAS

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    1. Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
    2. Goldman, Charles A. & Hopper, Nicole C. & Osborn, Julie G., 2005. "Review of US ESCO industry market trends: an empirical analysis of project data," Energy Policy, Elsevier, vol. 33(3), pages 387-405, February.
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    Cited by:

    1. Fan, Cheng & Lei, Yutian & Sun, Yongjun & Mo, Like, 2023. "Novel transformer-based self-supervised learning methods for improved HVAC fault diagnosis performance with limited labeled data," Energy, Elsevier, vol. 278(PB).

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