IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v227y2021ics0360544221007647.html
   My bibliography  Save this article

System-level fouling detection of district heating substations using virtual-sensor-assisted building automation system

Author

Listed:
  • Kim, Ryunhee
  • Hong, Yejin
  • Choi, Youngwoong
  • Yoon, Sungmin

Abstract

The operational fouling of heat exchangers in a district heating substation (DHS) has a negative effect on the heating efficiency in the primary district heating side and on the heating charge and thermal comfort in the terminal building side. It is effective for early detection of heat exchanger fouling when using the existing building automation system installed in a building. Therefore, an automated fouling detection method is proposed for DHSs. Practical challenges, including sensor absences and limited operation datasets, are considered for the proposed method to be effective for application in a real DHS at the building side. The assistance of virtual sensors is suggested to address the issue of sensor absences. The virtual measurements are fed into the input layer of the fouling detection model to reinforce the physical relationship of the trained model. Based on the physical relationship in the model, the virtual-sensor-assisted fouling detection method can significantly improve fouling detection performance for light fouling conditions. The detection is also successful for new DHS operation patterns that are not included in the training datasets. Compared with the application without a virtual sensor, the virtual-sensor-assisted application showed an approximately 61% improvement in accurate detection with a 17-min fast alarm.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s0360544221007647
    DOI: 10.1016/j.energy.2021.120515
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221007647
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.120515?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guelpa, Elisa & Marincioni, Ludovica & Verda, Vittorio, 2019. "Towards 4th generation district heating: Prediction of building thermal load for optimal management," Energy, Elsevier, vol. 171(C), pages 510-522.
    2. Leśko, Michał & Bujalski, Wojciech & Futyma, Kamil, 2018. "Operational optimization in district heating systems with the use of thermal energy storage," Energy, Elsevier, vol. 165(PA), pages 902-915.
    3. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    4. Aoun, Nadine & Bavière, Roland & Vallée, Mathieu & Aurousseau, Antoine & Sandou, Guillaume, 2019. "Modelling and flexible predictive control of buildings space-heating demand in district heating systems," Energy, Elsevier, vol. 188(C).
    5. 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.
    6. Guelpa, Elisa & Marincioni, Ludovica, 2019. "Demand side management in district heating systems by innovative control," Energy, Elsevier, vol. 188(C).
    7. Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    8. Koschwitz, D. & Frisch, J. & van Treeck, C., 2018. "Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale," Energy, Elsevier, vol. 165(PA), pages 134-142.
    9. Byun, Sun-Joon & Park, Hyun-Sik & Yi, Sung-Jae & Song, Chul-Hwa & Choi, Young-Don & Lee, So-Hyeon & Shin, Jong-Keun, 2015. "Study on the optimal heat supply control algorithm for district heating distribution network in response to outdoor air temperature," Energy, Elsevier, vol. 86(C), pages 247-256.
    10. Guelpa, Elisa & Verda, Vittorio, 2020. "Automatic fouling detection in district heating substations: Methodology and tests," Applied Energy, Elsevier, vol. 258(C).
    11. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    12. Noussan, Michel & Jarre, Matteo & Poggio, Alberto, 2017. "Real operation data analysis on district heating load patterns," Energy, Elsevier, vol. 129(C), pages 70-78.
    13. Guelpa, Elisa & Marincioni, Ludovica & Deputato, Stefania & Capone, Martina & Amelio, Stefano & Pochettino, Enrico & Verda, Vittorio, 2019. "Demand side management in district heating networks: A real application," Energy, Elsevier, vol. 182(C), pages 433-442.
    14. Gadd, Henrik & Werner, Sven, 2013. "Heat load patterns in district heating substations," Applied Energy, Elsevier, vol. 108(C), pages 176-183.
    15. Gadd, Henrik & Werner, Sven, 2015. "Fault detection in district heating substations," Applied Energy, Elsevier, vol. 157(C), pages 51-59.
    16. Lund, Henrik & Werner, Sven & Wiltshire, Robin & Svendsen, Svend & Thorsen, Jan Eric & Hvelplund, Frede & Mathiesen, Brian Vad, 2014. "4th Generation District Heating (4GDH)," Energy, Elsevier, vol. 68(C), pages 1-11.
    17. Dominković, D.F. & Gianniou, P. & Münster, M. & Heller, A. & Rode, C., 2018. "Utilizing thermal building mass for storage in district heating systems: Combined building level simulations and system level optimization," Energy, Elsevier, vol. 153(C), pages 949-966.
    18. Zhang, Qiang & Tian, Zhe & Ma, Zhijun & Li, Genyan & Lu, Yakai & Niu, Jide, 2020. "Development of the heating load prediction model for the residential building of district heating based on model calibration," Energy, Elsevier, vol. 205(C).
    19. Werner, Sven, 2017. "International review of district heating and cooling," Energy, Elsevier, vol. 137(C), pages 617-631.
    20. Gao, Dian-ce & Wang, Shengwei & Shan, Kui & Yan, Chengchu, 2016. "A system-level fault detection and diagnosis method for low delta-T syndrome in the complex HVAC systems," Applied Energy, Elsevier, vol. 164(C), pages 1028-1038.
    21. Marquant, Julien F. & Bollinger, L. Andrew & Evins, Ralph & Carmeliet, Jan, 2018. "A new combined clustering method to Analyse the potential of district heating networks at large-scale," Energy, Elsevier, vol. 156(C), pages 73-83.
    22. 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).
    23. Weber, Céline & Favrat, Daniel, 2010. "Conventional and advanced CO2 based district energy systems," Energy, Elsevier, vol. 35(12), pages 5070-5081.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
    2. Sun, Shaobo & Shan, Kui & Wang, Shengwei, 2022. "An online robust sequencing control strategy for identical chillers using a probabilistic approach concerning flow measurement uncertainties," Applied Energy, Elsevier, vol. 317(C).
    3. Hong, Yejin & Yoon, Sungmin & Choi, Sebin, 2023. "Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality," Energy, Elsevier, vol. 265(C).
    4. Jabeom Koo & Sungmin Yoon & Joowook Kim, 2022. "Virtual In Situ Calibration for Operational Backup Virtual Sensors in Building Energy Systems," Energies, MDPI, vol. 15(4), pages 1-12, February.
    5. Koo, Jabeom & Yoon, Sungmin, 2022. "In-situ sensor virtualization and calibration in building systems," Applied Energy, Elsevier, vol. 325(C).
    6. Dino, Giuseppe Edoardo & Catrini, Pietro & Buscemi, Alessandro & Piacentino, Antonio & Palomba, Valeria & Frazzica, Andrea, 2023. "Modeling of a bidirectional substation in a district heating network: Validation, dynamic analysis, and application to a solar prosumer," Energy, Elsevier, vol. 284(C).
    7. 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).
    8. Guelpa, E. & Capone, M. & Sciacovelli, A. & Vasset, N. & Baviere, R. & Verda, V., 2023. "Reduction of supply temperature in existing district heating: A review of strategies and implementations," Energy, Elsevier, vol. 262(PB).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Danica Djurić Ilić, 2020. "Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review," Energies, MDPI, vol. 14(1), pages 1-27, December.
    2. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
    3. Saletti, Costanza & Zimmerman, Nathan & Morini, Mirko & Kyprianidis, Konstantinos & Gambarotta, Agostino, 2021. "Enabling smart control by optimally managing the State of Charge of district heating networks," Applied Energy, Elsevier, vol. 283(C).
    4. Nord, Natasa & Shakerin, Mohammad & Tereshchenko, Tymofii & Verda, Vittorio & Borchiellini, Romano, 2021. "Data informed physical models for district heating grids with distributed heat sources to understand thermal and hydraulic aspects," Energy, Elsevier, vol. 222(C).
    5. Pavel Rušeljuk & Kertu Lepiksaar & Andres Siirde & Anna Volkova, 2021. "Economic Dispatch of CHP Units through District Heating Network’s Demand-Side Management," Energies, MDPI, vol. 14(15), pages 1-20, July.
    6. Guelpa, Elisa & Verda, Vittorio, 2021. "Demand response and other demand side management techniques for district heating: A review," Energy, Elsevier, vol. 219(C).
    7. Vivian, Jacopo & Quaggiotto, Davide & Zarrella, Angelo, 2020. "Increasing the energy flexibility of existing district heating networks through flow rate variations," Applied Energy, Elsevier, vol. 275(C).
    8. 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).
    9. 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).
    10. 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.
    11. Hong, Yejin & Yoon, Sungmin & Choi, Sebin, 2023. "Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality," Energy, Elsevier, vol. 265(C).
    12. Stanislav Chicherin & Vladislav Mašatin & Andres Siirde & Anna Volkova, 2020. "Method for Assessing Heat Loss in A District Heating Network with A Focus on the State of Insulation and Actual Demand for Useful Energy," Energies, MDPI, vol. 13(17), pages 1-15, September.
    13. Guelpa, E. & Capone, M. & Sciacovelli, A. & Vasset, N. & Baviere, R. & Verda, V., 2023. "Reduction of supply temperature in existing district heating: A review of strategies and implementations," Energy, Elsevier, vol. 262(PB).
    14. Kristensen, Martin Heine & Hedegaard, Rasmus Elbæk & Petersen, Steffen, 2020. "Long-term forecasting of hourly district heating loads in urban areas using hierarchical archetype modeling," Energy, Elsevier, vol. 201(C).
    15. 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.
    16. 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.
    17. Guelpa, Elisa & Verda, Vittorio, 2020. "Automatic fouling detection in district heating substations: Methodology and tests," Applied Energy, Elsevier, vol. 258(C).
    18. Calikus, Ece & Nowaczyk, Sławomir & Sant'Anna, Anita & Gadd, Henrik & Werner, Sven, 2019. "A data-driven approach for discovering heat load patterns in district heating," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    19. Angelidis, O. & Ioannou, A. & Friedrich, D. & Thomson, A. & Falcone, G., 2023. "District heating and cooling networks with decentralised energy substations: Opportunities and barriers for holistic energy system decarbonisation," Energy, Elsevier, vol. 269(C).
    20. Fester, Jakob & Østergaard, Peter Friis & Bentsen, Fredrik & Nielsen, Brian Kongsgaard, 2023. "A data-driven method for heat loss estimation from district heating service pipes using heat meter- and GIS data," Energy, Elsevier, vol. 277(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:227:y:2021:i:c:s0360544221007647. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.