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Real-world application of machine-learning-based fault detection trained with experimental data

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  • Bode, Gerrit
  • Thul, Simon
  • Baranski, Marc
  • Müller, Dirk

Abstract

Buildings are responsible for a large portion of the overall energy consumption. With the rising penetration of renewable energies, the heating and cooling demand of buildings will be increasingly satisfied by heat pumps. However, faults in the heat pump systems reduce energy efficiency or cause system failure, leading to an increased demand for primary energy. Hence, fault detection algorithms (FDA) are used to identify faults before system failure or efficiency deterioration occurs.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220304308
    DOI: 10.1016/j.energy.2020.117323
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    References listed on IDEAS

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    3. Østergaard, Dorte Skaarup & Smith, Kevin Michael & Tunzi, Michele & Svendsen, Svend, 2022. "Low-temperature operation of heating systems to enable 4th generation district heating: A review," Energy, Elsevier, vol. 248(C).
    4. Weigert, Andreas & Hopf, Konstantin & Günther, Sebastian A. & Staake, Thorsten, 2022. "Heat pump inspections result in large energy savings when a pre-selection of households is performed: A promising use case of smart meter data," Energy Policy, Elsevier, vol. 169(C).
    5. Simon P. Melgaard & Kamilla H. Andersen & Anna Marszal-Pomianowska & Rasmus L. Jensen & Per K. Heiselberg, 2022. "Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review," Energies, MDPI, vol. 15(12), pages 1-50, June.
    6. Zhong, Fangliang & Calautit, John Kaiser & Wu, Yupeng, 2023. "Fault data seasonal imbalance and insufficiency impacts on data-driven heating, ventilation and air-conditioning fault detection and diagnosis performances for energy-efficient building operations," Energy, Elsevier, vol. 282(C).
    7. Ferrara, Maria & Della Santa, Francesco & Bilardo, Matteo & De Gregorio, Alessandro & Mastropietro, Antonio & Fugacci, Ulderico & Vaccarino, Francesco & Fabrizio, Enrico, 2021. "Design optimization of renewable energy systems for NZEBs based on deep residual learning," Renewable Energy, Elsevier, vol. 176(C), pages 590-605.
    8. 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).
    9. William Nelson & Christopher Dieckert, 2024. "Machine Learning-Based Automated Fault Detection and Diagnostics in Building Systems," Energies, MDPI, vol. 17(2), pages 1-23, January.
    10. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2021. "Increasing resiliency against information vulnerability of renewable resources in the operation of smart multi-area microgrid," Energy, Elsevier, vol. 220(C).
    11. William Nelson & Charles Culp, 2022. "Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review," Energies, MDPI, vol. 15(15), pages 1-20, July.
    12. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    13. Schreiber, Thomas & Netsch, Christoph & Eschweiler, Sören & Wang, Tianyuan & Storek, Thomas & Baranski, Marc & Müller, Dirk, 2021. "Application of data-driven methods for energy system modelling demonstrated on an adaptive cooling supply system," Energy, Elsevier, vol. 230(C).

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