Skip to main content

Digital Twin-Based Fault Detection and Prioritisation in District Heating Systems: A Case Study in Denmark

  • Conference paper
  • First Online:
Energy Informatics (EI.A 2023)

Abstract

Faults in district heating systems (DHS) cause sub-optimal operating conditions, which increase energy losses. As DHSs are critical infrastructure for many households in Denmark, these faults should be detected and corrected quickly. A novel model-based fault detection and diagnosis framework has been applied to detect and prioritise faults. The framework uses a bound for normal operation based on the residuals between historical sensor data and simulated properties in a digital twin of the DHS. The faults detected are prioritised based on the fault probability calculated using the Chernoff bound method. A case study on a Danish DHS has proven that the framework can produce a prioritised list of faults that maintenance crews can use to target faults with the highest probability. Furthermore, the digital twin allowed for fault location investigation, which could correlate different faults in the DHS. The framework has the potential for real-time fault detection and diagnosis. However, more precise digital twins need to be developed.

This work is supported by the “Proactive and Predictive Maintenance of District Heating Systems” and “IEA DHC TS4”, funded by the Danish Energy Agency under the Energy Technology Development and Demonstration Program, ID number 64020-2102 and 134-22011, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alexandersen, E.K., Skydt, M.R., Engelsgaard, S.S., Bang, M., Jradi, M., Shaker, H.R.: A stair-step probabilistic approach for automatic anomaly detection in building ventilation system operation. Build. Environ. 157, 165–171 (2019). https://doi.org/10.1016/j.buildenv.2019.04.036

    Article  Google Scholar 

  2. Bahlawan, H., et al.: Detection and identification of faults in a district heating network. Energy Convers. Manage. 266, 115837 (2022). https://doi.org/10.1016/j.enconman.2022.115837

    Article  Google Scholar 

  3. Bang, M., Engelsgaard, S.S., Alexandersen, E.K., Skydt, M.R., Shaker, H.R., Jradi, M.: Novel real-time model-based fault detection method for automatic identification of abnormal energy performance in building ventilation units. Energy Build. 183, 238–251 (2019). https://doi.org/10.1016/j.enbuild.2018.11.006

    Article  Google Scholar 

  4. Buffa, S., Fouladfar, M.H., Franchini, G., Lozano Gabarre, I., Andrés Chicote, M.: Advanced control and fault detection strategies for district heating and cooling systems-a review. Appl. Sci. 11(1), 455 (2021). https://doi.org/10.3390/app11010455

    Article  Google Scholar 

  5. Cheung, B., Kumar, G., Rao, S.A.: Statistical algorithms in fault detection and prediction: toward a healthier network. Bell Labs Tech. J. 9(4), 171–185 (2005). https://doi.org/10.1002/bltj.20070

    Article  Google Scholar 

  6. EU: Commission recommendation (eu) 2019/786 of 8 May 2019 on building renovation (2019). https://bit.ly/30nxBs5

  7. Gadd, H., Werner, S.: Fault detection in district heating substations. Appl. Energy 157, 51–59 (2015). https://doi.org/10.1016/j.apenergy.2015.07.061

    Article  Google Scholar 

  8. Katipamula, S., Brambley, M.R.: Review article: methods for fault detection, diagnostics, and prognostics for building systems-a review, part II. HVAC &R Res. 11(2), 169–187 (2005). https://doi.org/10.1080/10789669.2005.10391133

    Article  Google Scholar 

  9. Kim, W., Katipamula, S.: A review of fault detection and diagnostics methods for building systems. Sci. Technol. Built Environ. 24(1), 3–21 (2018). https://doi.org/10.1080/23744731.2017.1318008

    Article  Google Scholar 

  10. Månsson, S., Kallioniemi, P.O.J., Sernhed, K., Thern, M.: A machine learning approach to fault detection in district heating substations. Energy Procedia 149, 226–235 (2018). https://doi.org/10.1016/j.egypro.2018.08.187. 16th International Symposium on District Heating and Cooling, DHC2018, 9-12 September 2018, Hamburg, Germany

  11. Pakanen, J., Hyvärinen, J., Kuismin, J., Ahonen, M.: Fault diagnosis methods for district heating substations. VTT Tiedotteita - Valtion Teknillinen Tutkimuskeskus (1996)

    Google Scholar 

  12. Sandin, F., Gustafsson, J., Delsing, J.: Fault detection with hourly district energy data: probabilistic methods and heuristics for automated detection and ranking of anomalies. Technical report, Svensk Fjärrvärme AB (2013)

    Google Scholar 

  13. Shaker, H.R., Santos, A., Jørgensen, B.: A practical data-driven condition indicator for room-level building diagnostics. Energy Inform. (2021)

    Google Scholar 

  14. Søndergaard, H.A.N., Shaker, H.R., Jørgensen, B.N.: Automated and real-time anomaly indexing for district heating maintenance decision support system. SSRN Electron. J. (2023). https://doi.org/10.2139/ssrn.4344182

  15. Sun, W., Cheng, D., Peng, W.: Anomaly detection analysis for district heating apartments. J. Appl. Sci. Eng. 21, 33–44 (2018). https://doi.org/10.6180/jase.201803_21(1).0005

    Article  Google Scholar 

  16. Webert, H., Döß, T., Kaupp, L., Simons, S.: Fault handling in industry 4.0: definition, process and applications. Sensors 22(6), 2205 (2022). https://doi.org/10.3390/s22062205

    Article  Google Scholar 

  17. Xue, P., et al.: Fault detection and operation optimization in district heating substations based on data mining techniques. Appl. Energy 205, 926–940 (2017). https://doi.org/10.1016/j.apenergy.2017.08.035

  18. Yu, W., Patros, P., Young, B., Klinac, E., Walmsley, T.G.: Energy digital twin technology for industrial energy management: classification, challenges and future. Renew. Sustain. Energy Rev. 161, 112407 (2022)

    Article  Google Scholar 

  19. Zimmerman, N., Dahlquist, E., Kyprianidis, K.: Towards on-line fault detection and diagnostics in district heating systems. Energy Procedia 105, 1960–1966 (2017). https://doi.org/10.1016/j.egypro.2017.03.567. 8th International Conference on Applied Energy, ICAE2016, 8-11 October 2016, Beijing, China

Download references

Acknowledgements

We thank Peer Andersen, Lasse Elmelund Pedersen, and the rest of their team at Fjernvarme Fyn A/S for their assistance with the data and the model. Also, thanks to Johan Peter Alsing from Danfoss A/S for assisting us with Leanheat Network.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Henrik Alexander Nissen Søndergaard .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Madsen, F.W., Bank, T., Søndergaard, H.A.N., Mortensen, L.K., Shaker, H.R. (2024). Digital Twin-Based Fault Detection and Prioritisation in District Heating Systems: A Case Study in Denmark. In: Jørgensen, B.N., da Silva, L.C.P., Ma, Z. (eds) Energy Informatics. EI.A 2023. Lecture Notes in Computer Science, vol 14468. Springer, Cham. https://doi.org/10.1007/978-3-031-48652-4_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48652-4_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48651-7

  • Online ISBN: 978-3-031-48652-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics