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

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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
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    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. 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.
    3. Gadd, Henrik & Werner, Sven, 2013. "Heat load patterns in district heating substations," Applied Energy, Elsevier, vol. 108(C), pages 176-183.
    4. Gadd, Henrik & Werner, Sven, 2015. "Fault detection in district heating substations," Applied Energy, Elsevier, vol. 157(C), pages 51-59.
    5. 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.
    6. 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.
    7. Gong, Mei & Werner, Sven, 2015. "An assessment of district heating research in China," Renewable Energy, Elsevier, vol. 84(C), pages 97-105.
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