Generation and evaluation of a synthetic dataset to improve fault detection in district heating and cooling systems
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DOI: 10.1016/j.energy.2023.128387
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References listed on IDEAS
- 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).
- Gadd, Henrik & Werner, Sven, 2015. "Fault detection in district heating substations," Applied Energy, Elsevier, vol. 157(C), pages 51-59.
- 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).
- 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).
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Cited by:
- 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).
- Leiria, Daniel & Johra, Hicham & Anoruo, Justus & Praulins, Imants & Piscitelli, Marco Savino & Capozzoli, Alfonso & Marszal-Pomianowska, Anna & Pomianowski, Michal Zbigniew, 2025. "Is it returning too hot? Time series segmentation and feature clustering of end-user substation faults in district heating systems," Applied Energy, Elsevier, vol. 381(C).
- 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).
- 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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Keywords
District heating and cooling; Synthetic dataset; Fault detection and diagnosis; Machine learning;All these keywords.
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