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Generation and evaluation of a synthetic dataset to improve fault detection in district heating and cooling systems

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  • Vallee, Mathieu
  • Wissocq, Thibaut
  • Gaoua, Yacine
  • Lamaison, Nicolas

Abstract

This paper investigates various types of faults in District Heating & Cooling (DHC) systems. Many authors point out that the lack of data hinders the development of good data-driven models for fault detection and diagnosis (FDD). In this work, we design a reference dataset based on simulation and use it to evaluate Machine Learning (ML) models for fault detection.

Suggested Citation

  • Vallee, Mathieu & Wissocq, Thibaut & Gaoua, Yacine & Lamaison, Nicolas, 2023. "Generation and evaluation of a synthetic dataset to improve fault detection in district heating and cooling systems," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223017814
    DOI: 10.1016/j.energy.2023.128387
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    References listed on IDEAS

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    1. Gadd, Henrik & Werner, Sven, 2015. "Fault detection in district heating substations," Applied Energy, Elsevier, vol. 157(C), pages 51-59.
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    Cited by:

    1. Jallal, Mohammed Ali & Vallée, Mathieu & Lamaison, Nicolas, 2024. "Fouling fault detection and diagnosis in district heating substations: Validation of a hybrid CNN-based PCA model with uncertainty quantification on virtual replica synthesis and real data," Energy, Elsevier, vol. 312(C).
    2. Bi, Yubo & Wu, Qiulan & Wang, Shilu & Shi, Jihao & Cong, Haiyong & Ye, Lili & Gao, Wei & Bi, Mingshu, 2023. "Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning," Energy, Elsevier, vol. 284(C).
    3. van Dreven, Jonne & Boeva, Veselka & Abghari, Shahrooz & Grahn, Håkan & Al Koussa, Jad, 2024. "A systematic approach for data generation for intelligent fault detection and diagnosis in District Heating," Energy, Elsevier, vol. 307(C).

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