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Automated Statistical Methods for Fault Detection in District Heating Customer Installations

Author

Listed:
  • Sara Månsson

    (Department of Energy Sciences, Faculty of Engineering, Lund University, P.O.Box 118, SE-221 00 Lund, Sweden
    Flemish Institute for Technological Research, VITO, Boeretang 200, BE-2400 Mol, Belgium)

  • Kristin Davidsson

    (Department of Energy Sciences, Faculty of Engineering, Lund University, P.O.Box 118, SE-221 00 Lund, Sweden)

  • Patrick Lauenburg

    (Department of Energy Sciences, Faculty of Engineering, Lund University, P.O.Box 118, SE-221 00 Lund, Sweden)

  • Marcus Thern

    (Department of Energy Sciences, Faculty of Engineering, Lund University, P.O.Box 118, SE-221 00 Lund, Sweden)

Abstract

In order to develop more sustainable district heating systems, the district heating sector is currently trying to increase the energy efficiency of these systems. One way of doing so is to identify customer installations in the systems that have poor cooling performance. This study aimed to develop an algorithm that was able to detect the poorly performing installations automatically using meter readings from the installations. The algorithm was developed using statistical methods and was tested on a data set consisting of data from 3000 installations located in a district heating system in Sweden. As many as 1273 installations were identified by the algorithm as having poor cooling performance. This clearly shows that it is of major interest to the district heating companies to identify the installations with poor cooling performance rapidly and automatically, in order to rectify them as soon as possible.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:113-:d:193969
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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).
    2. Søndergaard, Henrik Alexander Nissen & Shaker, Hamid Reza & Jørgensen, Bo Nørregaard, 2024. "Contextual operational energy performance indexing of district heating consumers," Energy, Elsevier, vol. 302(C).
    3. 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.

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