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Fault detection and operation optimization in district heating substations based on data mining techniques

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  1. Hong, Yejin & Yoon, Sungmin & Kim, Yong-Shik & Jang, Hyangin, 2021. "System-level virtual sensing method in building energy systems using autoencoder: Under the limited sensors and operational datasets," Applied Energy, Elsevier, vol. 301(C).
  2. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
  3. Li, Tingting & Zhou, Yangze & Zhao, Yang & Zhang, Chaobo & Zhang, Xuejun, 2022. "A hierarchical object oriented Bayesian network-based fault diagnosis method for building energy systems," Applied Energy, Elsevier, vol. 306(PB).
  4. Yunbo Yang & Rongling Li & Tao Huang, 2020. "Smart Meter Data Analysis of a Building Cluster for Heating Load Profile Quantification and Peak Load Shifting," Energies, MDPI, vol. 13(17), pages 1-20, August.
  5. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
  6. Sun, Chunhua & Zhang, Haixiang & Cao, Shanshan & Xia, Guoqiang & Zhong, Jian & Wu, Xiangdong, 2023. "A hierarchical classifying and two-step training strategy for detection and diagnosis of anormal temperature in district heating system," Applied Energy, Elsevier, vol. 349(C).
  7. Yan, Jingjing & Zhang, Huan & Wang, Yaran & Zhu, Zhaozhe & Bai, He & Li, Qicheng & Zheng, Lijun & Gao, Xinyong & You, Shijun, 2023. "Difference analysis and recognition of hydraulic oscillation by two types of sudden faults on long-distance district heating pipeline," Energy, Elsevier, vol. 284(C).
  8. Kim, Ryunhee & Hong, Yejin & Choi, Youngwoong & Yoon, Sungmin, 2021. "System-level fouling detection of district heating substations using virtual-sensor-assisted building automation system," Energy, Elsevier, vol. 227(C).
  9. Huang, Ke & Lu, Shilei & Han, Zhao & Yuan, Jianjuan, 2023. "Research on heat consumption detection, restoration and prediction methods for discontinuous heating substation," Energy, Elsevier, vol. 266(C).
  10. 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).
  11. Sungmin Yoon & Youngwoong Choi & Jabeom Koo & Yejin Hong & Ryunhee Kim & Joowook Kim, 2020. "Virtual Sensors for Estimating District Heating Energy Consumption under Sensor Absences in a Residential Building," Energies, MDPI, vol. 13(22), pages 1-13, November.
  12. Xue, Puning & Jiang, Yi & Zhou, Zhigang & Chen, Xin & Fang, Xiumu & Liu, Jing, 2019. "Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms," Energy, Elsevier, vol. 188(C).
  13. Alessandro Guzzini & Marco Pellegrini & Edoardo Pelliconi & Cesare Saccani, 2020. "Low Temperature District Heating: An Expert Opinion Survey," Energies, MDPI, vol. 13(4), pages 1-34, February.
  14. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
  15. Fan, Cheng & Sun, Yongjun & Shan, Kui & Xiao, Fu & Wang, Jiayuan, 2018. "Discovering gradual patterns in building operations for improving building energy efficiency," Applied Energy, Elsevier, vol. 224(C), pages 116-123.
  16. Hamidreza Alavi & Nuria Forcada, 2022. "User-Centric BIM-Based Framework for HVAC Root-Cause Detection," Energies, MDPI, vol. 15(10), pages 1-13, May.
  17. Tania Cerquitelli & Giovanni Malnati & Daniele Apiletti, 2019. "Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings," Energies, MDPI, vol. 12(15), pages 1-18, July.
  18. Yuwen You & Zhonghua Wang & Zhihao Liu & Chunmei Guo & Bin Yang, 2024. "Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network," Energies, MDPI, vol. 17(16), pages 1-19, August.
  19. Yuan, Jianjuan & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei & Zhou, Zhihua, 2022. "Evaluation of the operation data for improving the prediction accuracy of heating parameters in heating substation," Energy, Elsevier, vol. 238(PB).
  20. Neumayer, Martin & Stecher, Dominik & Grimm, Sebastian & Maier, Andreas & Bücker, Dominikus & Schmidt, Jochen, 2023. "Fault and anomaly detection in district heating substations: A survey on methodology and data sets," Energy, Elsevier, vol. 276(C).
  21. Sun, Chunhua & Yuan, Lingyu & Cao, Shanshan & Xia, Guoqiang & Liu, Yanan & Wu, Xiangdong, 2023. "Identifying supply-demand mismatches in district heating system based on association rule mining," Energy, Elsevier, vol. 280(C).
  22. Huang, Ke & Yuan, Jianjuan & Zhou, Zhihua & Zheng, Xuejing, 2022. "Analysis and evaluation of heat source data of large-scale heating system based on descriptive data mining techniques," Energy, Elsevier, vol. 251(C).
  23. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
  24. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
  25. Guelpa, Elisa & Verda, Vittorio, 2020. "Automatic fouling detection in district heating substations: Methodology and tests," Applied Energy, Elsevier, vol. 258(C).
  26. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
  27. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
  28. Zhang, Chaobo & Xue, Xue & Zhao, Yang & Zhang, Xuejun & Li, Tingting, 2019. "An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  29. Fan, Cheng & Xiao, Fu & Zhao, Yang & Wang, Jiayuan, 2018. "Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data," Applied Energy, Elsevier, vol. 211(C), pages 1123-1135.
  30. Sara Månsson & Kristin Davidsson & Patrick Lauenburg & Marcus Thern, 2018. "Automated Statistical Methods for Fault Detection in District Heating Customer Installations," Energies, MDPI, vol. 12(1), pages 1-18, December.
  31. Xuebin Ma & Junfeng Li & Yucheng Ren & Reaihan E & Qiugang Wang & Jie Li & Sihui Huang & Mingguo Ma, 2022. "Performance and Economic Analysis of the Multi-Energy Complementary Heating System under Different Control Strategies in Cold Regions," Energies, MDPI, vol. 15(21), pages 1-17, November.
  32. Shahrooz Abghari & Veselka Boeva & Jens Brage & Håkan Grahn, 2020. "A Higher Order Mining Approach for the Analysis of Real-World Datasets," Energies, MDPI, vol. 13(21), pages 1-23, November.
  33. Mengyao Lu & Guitao Xu & Jianjuan Yuan, 2023. "Installation Principle and Calculation Model of the Representative Indoor Temperature-Monitoring Points in Large-Scale Buildings," Energies, MDPI, vol. 16(17), pages 1-19, September.
  34. Yuan, Jianjuan & Zhou, Zhihua & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei, 2021. "Analysis and evaluation of the operation data for achieving an on-demand heating consumption prediction model of district heating substation," Energy, Elsevier, vol. 214(C).
  35. Månsson, Sara & Johansson Kallioniemi, Per-Olof & Thern, Marcus & Van Oevelen, Tijs & Sernhed, Kerstin, 2019. "Faults in district heating customer installations and ways to approach them: Experiences from Swedish utilities," Energy, Elsevier, vol. 180(C), pages 163-174.
  36. Sara Månsson & Marcus Thern & Per-Olof Johansson Kallioniemi & Kerstin Sernhed, 2021. "A Fault Handling Process for Faults in District Heating Customer Installations," Energies, MDPI, vol. 14(11), pages 1-18, May.
  37. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
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