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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. Bode, Gerrit & Thul, Simon & Baranski, Marc & Müller, Dirk, 2020. "Real-world application of machine-learning-based fault detection trained with experimental data," Energy, Elsevier, vol. 198(C).
    2. Leoni, Paolo & Geyer, Roman & Schmidt, Ralf-Roman, 2020. "Developing innovative business models for reducing return temperatures in district heating systems: Approach and first results," Energy, Elsevier, vol. 195(C).
    3. Gadd, Henrik & Werner, Sven, 2015. "Fault detection in district heating substations," Applied Energy, Elsevier, vol. 157(C), pages 51-59.
    4. Yang, Xilian & Zhao, Qunfei & Wang, Yuzhang & Cheng, Kanru, 2023. "Fault signal reconstruction for multi-sensors in gas turbine control systems based on prior knowledge from time series representation," Energy, Elsevier, vol. 262(PA).
    Full references (including those not matched with items on IDEAS)

    Citations

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    1. Luo, Lisheng & Xie, Junlong & Zhou, Xuan & Chen, Jianye & Xiong, Jiaming, 2026. "A two-stage leakage diagnosis framework for circulating cooling water pipe networks integrating machine learning and parameter estimation," Energy, Elsevier, vol. 344(C).
    2. 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).
    3. Cao, Shanshan & Yang, Shaochuan & Sun, Chunhua & Zhang, Haixiang & Wu, Xiangdong, 2025. "Integrating multidimensional operational parameters for abnormal diagnosis in substations: A composite approach of non-uniform time series segmentation, trend information extraction, and symbolic representation," Energy, Elsevier, vol. 335(C).
    4. 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).
    5. Liu, Yang & Ren, Qingqing, 2025. "Online incremental learning approach of heat pump and chiller models based on the dynamic random forests in queue structure," Energy, Elsevier, vol. 323(C).
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    7. Schmidt, Dietrich & Yang, Qinjiang & Vanhoudt, Dirk & Widl, Edmund & Langroudi, Pakdad & Vallee, Mathieu & Jallal, Mohammed-Ali & Muschick, Daniel & Gölles, Markus & Kaisermayer, Valentin & Tunzi, Mic, 2026. "The current state and future outlook of digitalization for the operation of district heating systems: A review," Energy, Elsevier, vol. 344(C).
    8. 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).
    9. Guevara Bastidas, Edison & Faulstich, Stefan & Dittmer, Holger & Neumayer, Martin & Mohan, Gowtham Sakthivel & Sercan-Calismaz, Kibriye & Hosenfelder, Frank & Glenewinkel, Thilo & Fischer-Florschütz, , 2025. "Prioritisation of faults in district heating substations: Towards predictive maintenance and optimised operation," Energy, Elsevier, vol. 333(C).
    10. van Dreven, Jonne & Cheddad, Abbas & Alawadi, Sadi & Ghazi, Ahmad Nauman & Koussa, Jad Al & Vanhoudt, Dirk, 2025. "From bearings to substations: Transfer Learning for fault detection in district heating," Energy, Elsevier, vol. 335(C).

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