A performance assessment method for district cooling substations based on operational data

References

• Abghari, S., V. Boeva, J. Brage, and C. Johansson. 2020. District heating substation behaviour modelling for annotating the performance. In Machine learning and knowledge discovery in databases. Vol. 1168. Springer International Publishing. doi:10.1007/978-3-030-43887-6
   Google Scholar
• Abghari, S., V. Boeva, J. Brage, C. Johansson, H. Grahn, and N. Lavesson. 2019. Higher order mining for monitoring district heating substations. Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, 382–91. doi:10.1109/DSAA.2019.00053
   Google Scholar
• Audivet Durán, C., and M. E. Sanjuán. 2016. On-line early fault detection of a centrifugal chiller based on data driven approach. Proceedings of the ASME 2016 10th International Conference on Energy Sustainability, Charlotte, NC. doi:10.1115/ES2016-59291
   Google Scholar
• Bergstraesser, W., A. Hinz, H. Braas, J. Orozaliev, and K. Vajen. 2021. Lessons learned from excess flow analyses for various district heating systems. Smart Energy 1:100005. doi:10.1016/j.segy.2021.100005
   Google Scholar
• Bode, G., S. Thul, M. Baranski, and D. Müller. 2020. Real-world application of machine-learning-based fault detection trained with experimental data. Energy 198:117323. doi:10.1016/j.energy.2020.117323
   Web of Science ®Google Scholar
• Brès, A., C. Johansson, R. Geyer, P. Leoni, and J. Sjögren. 2019. Coupled building and system simulations for detection and diagnosis of high district heating return temperatures. Building Simulation Conference Proceedings, vol. 3: 1874–81. doi:10.26868/25222708.2019.210629
   Google Scholar
• Bruton, K., P. Raftery, B. Kennedy, M. M. Keane, and D. T. J. O’Sullivan. 2014. Review of automated fault detection and diagnostic tools in air handling units. Energy Efficiency 7 (2):335–51. doi:10.1007/s12053-013-9238-2
   Web of Science ®Google Scholar
• Buffa, S., M. F. Hossein, G. Franchini, I. G. Lozano, and M. A. Chicote. 2021. Advanced control and fault detection strategies for district heating and cooling systems—A review. Applied Sciences 11 (1):455. doi:10.3390/app11010455
   Google Scholar
• Calikus, E., S. Nowaczyk, A. Sant’Anna, and S. Byttner. 2018. Ranking abnormal substations by power signature dispersion. Energy Procedia.149:345–53. doi:10.1016/j.egypro.2018.08.198
   Google Scholar
• Chen, Y., and L. Lan. 2010. Fault detection, diagnosis and data recovery for a real building heating/cooling billing system. Energy Conversion and Management 51 (5):1015–24. doi:10.1016/j.enconman.2009.12.004
   Web of Science ®Google Scholar
• Dai, M., X. Lu, and P. Xu. 2021. Causes of low delta-T syndrome for chilled water systems in buildings. Journal of Building Engineering 33 (May 2020):101499. doi:10.1016/j.jobe.2020.101499
   Google Scholar
• Du, Z., B. Fan, X. Jin, and J. Chi. 2014. Fault detection and diagnosis for buildings and hvac systems using combined neural networks and subtractive clustering analysis. Building and Environment 73:1–11. doi:10.1016/j.buildenv.2013.11.021
   Web of Science ®Google Scholar
• Fan, C., F. Xiao, Z. Li, and J. Wang. 2018. Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: A review. Energy and Buildings 159:296–308. doi:10.1016/j.enbuild.2017.11.008
   Web of Science ®Google Scholar
• Farouq, S., S. Byttner, M. R. Bouguelia, N. Nord, and H. Gadd. 2020. Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach. Engineering Applications of Artificial Intelligence 90 (December 2019):103492. doi:10.1016/j.engappai.2020.103492
   Google Scholar
• Gadd, H., and S. Werner. 2014. Achieving low return temperatures from district heating substations. Applied Energy 136:59–67. doi:10.1016/j.apenergy.2014.09.022
   Web of Science ®Google Scholar
• Gadd, H., and S. Werner. 2015. Fault detection in district heating substations. Applied Energy 157:51–9. doi:10.1016/j.apenergy.2015.07.061
   Web of Science ®Google Scholar
• Gao, D.-c., S. Wang, W. Gang, and F. Xiao. 2016. A model-based adaptive method for evaluating the energy impact of low delta-T syndrome in complex HVAC systems using support vector regression. Building Services Engineering Research and Technology 37 (5):573–96. doi:10.1177/0143624416640760
   Web of Science ®Google Scholar
• Gao, D.-c., S. Wang, K. Shan, and C. Yan. 2016. A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems. Applied Energy 164:1028–38. doi:10.1016/j.apenergy.2015.02.025
   Web of Science ®Google Scholar
• Gao, D.-c., S. Wang, Y. Sun, and F. Xiao. 2012. Diagnosis of the low temperature difference syndrome in the chilled water system of a super high-rise building: A case study. Applied Energy 98:597–606. doi:10.1016/j.apenergy.2012.03.057
   Web of Science ®Google Scholar
• Gao, D.-c., S. Wang, and K. Shan. 2016. In-situ implementation and evaluation of an online robust pump speed control strategy for avoiding low delta-T syndrome in complex chilled water systems of high-rise buildings. Applied Energy 171:541–54. doi:10.1016/j.apenergy.2016.03.077
   Web of Science ®Google Scholar
• Göteborg Energi AB. 2021. Lokala Anvisningar För Fjärrkyla.
   Google Scholar
• Guelpa, E., and V. Verda. 2020. Automatic fouling detection in district heating substations: Methodology and tests. Applied Energy 258 (February 2019):114059. doi:10.1016/j.apenergy.2019.114059
   Google Scholar
• Gustafsson, J., and F. Sandin. 2015. District heating monitoring and control systems. Advanced district heating and cooling (DHC) systems. Luleå, Sweden: Elsevier Ltd. doi:10.1016/B978-1-78242-374-4.00012-4
   Google Scholar
• IEA. 2018. The future of cooling: Opportunities for energy-efficient air conditioning. Paris: IEA. doi:10.1787/9789264301993-en
   Google Scholar
• Incropera, F. P., D. P. Dewitt, T. L. Bergman, and A. S. Lavine. 2007. Fundamentals of heat and mass transfer. 6th ed. Hoboken, NJ: John Wiley & Sons, Inc.
   Google Scholar
• Jangsten, M. 2020. Gothenburg district cooling system - An evaluation of the system performance based on operational data. Licentiate Thesis, Chalmers University of Technology.
   Google Scholar
• Jangsten, M., P. Filipsson, T. Lindholm, and J.-O. Dalenbäck. 2020. High temperature district cooling: Challenges and possibilities based on an existing district cooling system and its connected buildings. Energy 199 (May):117407. doi:10.1016/j.energy.2020.117407
   Google Scholar
• Jangsten, M., T. Lindholm, and J. O. Dalenbäck. 2020. Analysis of operational data from a district cooling system and its connected buildings. Energy 203:117844. doi:10.1016/j.energy.2020.117844
   Web of Science ®Google Scholar
• Jangsten, M., T. Lindholm, and J. O. Dalenbäck. 2021. Field test of active night cooling supplied by district cooling in three commercial buildings. E3S Web of Conferences. Vol. 246. doi:10.1051/e3sconf/202124611004
   Google Scholar
• Jangsten, M., T. Lindholm, and J. O. Dalenbäck. 2022. District cooling substation design and control to achieve high return temperatures. Chalmers University of Technology - Division of Building Services Engineering [Manuscript submitted for publication]. doi:https://dx.doi.org/10.2139/ssrn.3999151
   Google Scholar
• Kiluk, S. 2014. Dynamic classification system in large-scale supervision of energy efficiency in buildings. Applied Energy 132:1–14. doi:10.1016/j.apenergy.2014.06.054
   Web of Science ®Google Scholar
• Kiluk, S. 2012. Algorithmic acquisition of diagnostic patterns in district heating billing system. Applied Energy 91 (1):146–55. doi:10.1016/j.apenergy.2011.09.023
   Web of Science ®Google Scholar
• Kiluk, S. 2017. Diagnostic information system dynamics in the evaluation of machine learning algorithms for the supervision of energy efficiency of district heating-supplied buildings. Energy Conversion and Management 150:904–13. doi:10.1016/j.enconman.2017.05.006
   Web of Science ®Google Scholar
• Lee, T., S. Yoon, and K. Won. 2022. Delta-T-based operational signatures for operation pattern and fault diagnosis of building energy systems. Energy and Buildings 257:111769. doi:10.1016/j.enbuild.2021.111769
   Web of Science ®Google Scholar
• Li, G., Y. Hu, J. Liu, X. Fang, and J. Kang. 2021. Review on fault detection and diagnosis feature engineering in building heating, ventilation, air conditioning and refrigeration systems. IEEE Access 9:2153–87. doi:10.1109/ACCESS.2020.3040980
   Web of Science ®Google Scholar
• Li, M., W. Deng, K. Xiahou, T. Ji, and Q. Wu. 2020. A data-driven method for fault detection and isolation of the integrated energy-based district heating system. IEEE Access 8:23787–801. doi:10.1109/ACCESS.2020.2970273
   Web of Science ®Google Scholar
• Li, Y., and Z. O’Neill. 2018. A critical review of fault modeling of HVAC systems in buildings. Building Simulation 11 (5):953–75. doi:10.1007/s12273-018-0458-4
   Web of Science ®Google Scholar
• Liang, J., and R. Du. 2007. Model-based fault detection and diagnosis of HVAC systems using support vector machine method. International Journal of Refrigeration 30 (6):1104–14. doi:10.1016/j.ijrefrig.2006.12.012
   Web of Science ®Google Scholar
• Lin, X., Z. Tian, Y. Lu, J. Niu, and Y. Cao. 2022. An energy performance assessment method for district heating substations based on energy disaggregation. Energy and Buildings 255:111615. doi:10.1016/j.enbuild.2021.111615
   Web of Science ®Google Scholar
• Lu, Y., Z. Tian, P. Peng, J. Niu, and J. Dai. 2019. Identification and evaluation of operation regulation strategies in district heating substations based on an unsupervised data mining method. Energy and Buildings 202:109324. doi:10.1016/j.enbuild.2019.07.041
   Web of Science ®Google Scholar
• Luo, X. J., K. F. Fong, Y. J. Sun, and M. K. Leung. 2019. Development of clustering-based sensor fault detection and diagnosis strategy for chilled water system. Energy and Buildings 186:17–36. doi:10.1016/j.enbuild.2019.01.006
   Web of Science ®Google Scholar
• Månsson, S., K. Davidsson, P. Lauenburg, and M. Thern. 2018. Automated statistical methods for fault detection in district heating customer installations. Energies 12 (1):113–8. doi:10.3390/en12010113
   Web of Science ®Google Scholar
• Månsson, S., P. O. J. Kallioniemi, K. Sernhed, and M. Thern. 2018. A machine learning approach to fault detection in district heating substations. Energy Procedia 149:226–35. doi:10.1016/j.egypro.2018.08.187
   Google Scholar
• Månsson, S., P. O. J. Kallioniemi, M. Thern, T. Van Oevelen, and K. Sernhed. 2019. Faults in district heating customer installations and ways to approach them: Experiences from Swedish utilities. Energy 180:163–74. doi:10.1016/j.energy.2019.04.220
   Web of Science ®Google Scholar
• Månsson, S., I. L. Benzi, M. Thern, R. Salenbien, K. Sernhed, and P.-O J. Kallioniemi. 2021. A taxonomy for labeling deviations in district heating customer data. Smart Energy 2:100020. doi:10.1016/j.segy.2021.100020
   Google Scholar
• Månsson, S., M. Thern, P.-o. J. Kallioniemi, and K. Sernhed. 2021. A fault handling process for faults in district heating customer installations. Energies 14 (11):3169. doi:10.3390/en14113169
   Web of Science ®Google Scholar
• McIntosh, I. 1999. A model-based fault detection and diagnosis methodology for HVAC subsystems. University of Wisconsin.
   Google Scholar
• Miller, C., Z. Nagy, and A. Schlueter. 2018. A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings. Renewable and Sustainable Energy Reviews 81 (May 2017):1365–77. doi:10.1016/j.rser.2017.05.124
   Google Scholar
• Mirnaghi Sadat, M., and F. Haghighat. 2020. Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review. Energy and Buildings 229:110492. doi:10.1016/j.enbuild.2020.110492
   Web of Science ®Google Scholar
• Namburu, S. M., M. S. Azam, J. Luo, K. Choi, and K. R. Pattipati. 2007. Data-driven modeling, fault diagnosis and optimal sensor selection for HVAC chillers. IEEE Transactions on Automation Science and Engineering 4 (3):469–73. doi:10.1109/TASE.2006.888053
   Web of Science ®Google Scholar
• Ntakolia, C., A. Anagnostis, S. Moustakidis, and N. Karcanias. 2022. Machine learning applied on the district heating and cooling sector: A review. Energy Systems 13 (1):1–30. doi:10.1007/s12667-020-00405-9
   Google Scholar
• Pakanen, J., J. Hyvärinen, J. Kuismin, and M. Ahonen. 1996. Fault diagnosis methods for district heating substations. Oulu, Finland: VTT.
   Google Scholar
• Petersson, S., and C. Dahlberg-Larsson. 2013. Samband Mellan Flödespremie Och Returtemperatur. Svensk Fjärrvärme AB.
   Google Scholar
• Renström, J., J. Johnsson, and K. Ludvig. 2021. Kartläggning Av Kylamarknaden. Värmemarknad Sverige, no. December.
   Google Scholar
• Thuillard, M., F. Reider, and G. P. Henze. 2014. Energy efficiency strategies for hydronic systems through intelligent actuators. In 2014 ASHRAE Winter Conference, 1–8.
   Google Scholar
• Verhelst, J., G. Van Ham, D. Saelens, and L. Helsen. 2017. Model selection for continuous commissioning of HVAC-systems in office buildings: A review. Renewable and Sustainable Energy Reviews 76 (March):673–86. doi:10.1016/j.rser.2017.01.119
   Google Scholar
• Walletun, H., and J. Johnsson. 2005. Grundläggande Faktorer För Lyckosamma Avkylningsprojekt. Svensk Fjärrvärme AB.
   Google Scholar
• Wang, P., R. X. Gao, and Z. Fan. 2015. Switching local search particle filtering for heat exchanger degradation prognosis. Conference Record - IEEE Instrumentation and Measurement Technology Conference, 2015-July: 539–44. doi:10.1109/I2MTC.2015.7151325
   Google Scholar
• Wang, P., and R. X. Gao. 2017. Automated performance tracking for heat exchangers in HVAC. IEEE Transactions on Automation Science and Engineering 14 (2):1–12.
   Web of Science ®Google Scholar
• Wang, S., and J. Cui. 2005. Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method. Applied Energy 82 (3):197–213. doi:10.1016/j.apenergy.2004.11.002
   Web of Science ®Google Scholar
• Wang, S., Q. Zhou, and F. Xiao. 2010. A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults. Energy and Buildings 42 (4):477–90. doi:10.1016/j.enbuild.2009.10.017
   Web of Science ®Google Scholar
• Xue, P., Z. Zhou, X. Fang, X. Chen, L. Liu, Y. Liu, and J. Liu. 2017. Fault detection and operation optimization in district heating substations based on data mining techniques. Applied Energy 205 (73):926–40. doi:10.1016/j.apenergy.2017.08.035
   Google Scholar
• Yu, Y., D. Woradechjumroen, and D. Yu. 2014. A review of fault detection and diagnosis methodologies on air-handling units. Energy and Buildings 82:550–62. doi:10.1016/j.enbuild.2014.06.042
   Web of Science ®Google Scholar
• Zhang, F., and H. Fleyeh. 2020. Anomaly detection of heat energy usage in district heating substations using LSTM based variational autoencoder combined with physical model. In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 153–58. doi:10.1109/ICIEA48937.2020.9248108.
   Google Scholar
• Zhang, R., and T. Hong. 2017. Modeling of HVAC operational faults in building performance simulation. Applied Energy 202:178–88. doi:10.1016/j.apenergy.2017.05.153
   Web of Science ®Google Scholar
• Zhao, X., M. Yang, and H. Li. 2014. Field implementation and evaluation of a decoupling-based fault detection and diagnostic method for chillers. Energy and Buildings 72:419–30. doi:10.1016/j.enbuild.2014.01.003
   Web of Science ®Google Scholar
• Zhao, Y., T. Li, X. Zhang, and C. Zhang. 2019. Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renewable and Sustainable Energy Reviews 109 (April):85–101. doi:10.1016/j.rser.2019.04.021
   Google Scholar
• Zhao, Y., S. Wang, and F. Xiao. 2013a. A statistical fault detection and diagnosis method for centrifugal chillers based on exponentially-weighted moving average control charts and support vector regression. Applied Thermal Engineering 51 (1–2):560–72. doi:10.1016/j.applthermaleng.2012.09.030
   Web of Science ®Google Scholar
• Zhao, Y., S. Wang, and F. Xiao. 2013b. A system-level incipient fault-detection method for HVAC systems. HVAC and R Research 19 (5):593–601. doi:10.1080/10789669.2013.789371
   Web of Science ®Google Scholar
• Zhao, Y., F. Xiao, and S. Wang. 2013. An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network. Energy and Buildings 57:278–88. doi:10.1016/j.enbuild.2012.11.007
   Web of Science ®Google Scholar
• Zhou, Q., S. Wang, and Z. Ma. 2009. A model-based fault detection and diagnosis strategy for HVAC systems. International Journal of Energy Research 33 (10):903–18. doi:10.1002/er
   Web of Science ®Google Scholar
• Zimmerman, N., E. Dahlquist, and K. Kyprianidis. 2017. Towards on-line fault detection and diagnostics in district heating systems. Energy Procedia 105:1960–6. doi:10.1016/j.egypro.2017.03.567
   Google Scholar
• Zinko, H., L. Hoon, K. Bong-Kyun, K. Youn-Hong, H. Lindkvist, A. Loewen, H. Walletun, and M. Wigbels. 2005. IEA-DHC-project: Improvement of operational temperature differences in district heating systems. Sittard, The Netherlands. http://www.verenum.ch/Dokumente/2005_IEA_ImprovementDT.pdf.
   Google Scholar