IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i6p2709-d1097033.html
   My bibliography  Save this article

A New Time-Series Fluctuation Study Method Applied to Flow and Pressure Data in a Heating Network

Author

Listed:
  • Shuai Zhao

    (School of Architecture, Harbin Institute of Technology, Harbin 150090, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China)

  • Huizhe Cao

    (School of Architecture, Harbin Institute of Technology, Harbin 150090, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China)

  • Jiguang Zhu

    (Flow Measurement Research Center, Harbin Institute of Metrology, Harbin 150036, China)

  • Jinxiang Chen

    (School of Management, Harbin Institute of Technology, Harbin 150090, China)

  • Chein-Chi Chang

    (School of Architecture, Harbin Institute of Technology, Harbin 150090, China
    Department of Chemical, Biochemical, and Environmental Engineering, University of Maryland, Baltimore, MD 21250, USA)

Abstract

The key to achieving smart heating is the rational use of large amounts of data from the heating network. However, many current relevant studies based on generalized mathematical methods are unable to accurately describe the physical relationships between pipe network variables. In order to solve this problem, this paper proposes a new time-series fluctuation research method, which can be applied to the measured data of the hot water heating pipe network. This method is a new approach to identifying step data. Then, we propose the concept of time-series disturbance to quantify the degree of data anomaly. Finally, the results of a case study demonstrate the transfer process of a significant disturbance in the pipe network from the supply end to the return end. The time-series fluctuation method in this paper precisely describes two physical relationships between heating system variables and provides a feasible and convenient new research idea for self-perception and self-analysis of smart heating.

Suggested Citation

  • Shuai Zhao & Huizhe Cao & Jiguang Zhu & Jinxiang Chen & Chein-Chi Chang, 2023. "A New Time-Series Fluctuation Study Method Applied to Flow and Pressure Data in a Heating Network," Energies, MDPI, vol. 16(6), pages 1-18, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2709-:d:1097033
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/6/2709/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/6/2709/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wu, Wei & Wang, Baolong & Shi, Wenxing & Li, Xianting, 2014. "Absorption heating technologies: A review and perspective," Applied Energy, Elsevier, vol. 130(C), pages 51-71.
    2. Gong, Mei & Werner, Sven, 2015. "An assessment of district heating research in China," Renewable Energy, Elsevier, vol. 84(C), pages 97-105.
    3. Zhang, Qunli & Zhang, Lin & Nie, Jinzhe & Li, Yinlong, 2017. "Techno-economic analysis of air source heat pump applied for space heating in northern China," Applied Energy, Elsevier, vol. 207(C), pages 533-542.
    4. Gadd, Henrik & Werner, Sven, 2013. "Heat load patterns in district heating substations," Applied Energy, Elsevier, vol. 108(C), pages 176-183.
    5. Gadd, Henrik & Werner, Sven, 2015. "Fault detection in district heating substations," Applied Energy, Elsevier, vol. 157(C), pages 51-59.
    6. 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.
    7. Du, Zhimin & Jin, Xinqiao & Yang, Yunyu, 2009. "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network," Applied Energy, Elsevier, vol. 86(9), pages 1624-1631, September.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Ma, Sining & Guo, Siyue & Zheng, Dingqian & Chang, Shiyan & Zhang, Xiliang, 2021. "Roadmap towards clean and low carbon heating to 2035: A provincial analysis in northern China," Energy, Elsevier, vol. 225(C).
    4. Sernhed, Kerstin & Lygnerud, Kristina & Werner, Sven, 2018. "Synthesis of recent Swedish district heating research," Energy, Elsevier, vol. 151(C), pages 126-132.
    5. 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.
    6. 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).
    7. 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).
    8. Averfalk, Helge & Werner, Sven, 2018. "Novel low temperature heat distribution technology," Energy, Elsevier, vol. 145(C), pages 526-539.
    9. Østergaard, Dorte Skaarup & Svendsen, Svend, 2018. "Experience from a practical test of low-temperature district heating for space heating in five Danish single-family houses from the 1930s," Energy, Elsevier, vol. 159(C), pages 569-578.
    10. Zhang, Fan & Bales, Chris & Fleyeh, Hasan, 2021. "Night setback identification of district heat substations using bidirectional long short term memory with attention mechanism," Energy, Elsevier, vol. 224(C).
    11. 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.
    12. Østergaard, Dorte Skaarup & Tunzi, Michele & Svendsen, Svend, 2021. "What does a well-functioning heating system look like? Investigation of ten Danish buildings that utilize district heating efficiently," Energy, Elsevier, vol. 227(C).
    13. Gong, Mei & Ottermo, Fredric, 2022. "High-temperature thermal storage in combined heat and power plants," Energy, Elsevier, vol. 252(C).
    14. 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.
    15. Ioan Sarbu & Matei Mirza & Daniel Muntean, 2022. "Integration of Renewable Energy Sources into Low-Temperature District Heating Systems: A Review," Energies, MDPI, vol. 15(18), pages 1-28, September.
    16. 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.
    17. Theofanis Benakopoulos & Robbe Salenbien & Dirk Vanhoudt & Svend Svendsen, 2019. "Improved Control of Radiator Heating Systems with Thermostatic Radiator Valves without Pre-Setting Function," Energies, MDPI, vol. 12(17), pages 1-24, August.
    18. 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).
    19. Østergaard, Dorte Skaarup & Svendsen, Svend, 2016. "Replacing critical radiators to increase the potential to use low-temperature district heating – A case study of 4 Danish single-family houses from the 1930s," Energy, Elsevier, vol. 110(C), pages 75-84.
    20. Marco Pellegrini & Augusto Bianchini, 2018. "The Innovative Concept of Cold District Heating Networks: A Literature Review," Energies, MDPI, vol. 11(1), pages 1-16, January.

    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:gam:jeners:v:16:y:2023:i:6:p:2709-:d:1097033. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.