IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i14p3511-d1437107.html
   My bibliography  Save this article

Prediction of Pipe Failure Rate in Heating Networks Using Machine Learning Methods

Author

Listed:
  • Hristo Ivanov Beloev

    (Department Agricultural Machinery, “Angel Kanchev” University of Ruse, 7017 Ruse, Bulgaria)

  • Stanislav Radikovich Saitov

    (Department Nuclear and Thermal Power Plants, Kazan State Power Engineering University, 420066 Kazan, Russia)

  • Antonina Andreevna Filimonova

    (Department Nuclear and Thermal Power Plants, Kazan State Power Engineering University, 420066 Kazan, Russia)

  • Natalia Dmitrievna Chichirova

    (Department Nuclear and Thermal Power Plants, Kazan State Power Engineering University, 420066 Kazan, Russia)

  • Oleg Evgenievich Babikov

    (Department Nuclear and Thermal Power Plants, Kazan State Power Engineering University, 420066 Kazan, Russia)

  • Iliya Krastev Iliev

    (Department of Heat, Hydraulics and Environmental Engineering, “Angel Kanchev” University of Ruse, 7017 Ruse, Bulgaria)

Abstract

The correct prediction of heating network pipeline failure rates can increase the reliability of the heat supply to consumers in the cold season. However, due to the large number of factors affecting the corrosion of underground steel pipelines, it is difficult to achieve high prediction accuracy. The purpose of this study is to identify connections between the failure rate of heating network pipelines and factors not taken into account in traditional methods, such as residual pipeline wall thickness, soil corrosion activity, previous incidents on the pipeline section, flooding (traces of flooding) of the channel, and intersections with communications. To achieve this goal, the following machine learning algorithms were used: random forest, gradient boosting, support vector machines, and artificial neural networks (multilayer perceptron). The data were collected on incidents related to the breakdown of heating network pipelines in the cities of Kazan and Ulyanovsk. Based on these data, four intelligent models have been developed. The accuracy of the models was compared. The best result was obtained for the gradient boosting regression tree, as follows: MSE = 0.00719, MAE = 0.0682, and MAPE = 0.06069. The feature «Previous incidents on the pipeline section» was excluded from the training set as the least significant.

Suggested Citation

  • Hristo Ivanov Beloev & Stanislav Radikovich Saitov & Antonina Andreevna Filimonova & Natalia Dmitrievna Chichirova & Oleg Evgenievich Babikov & Iliya Krastev Iliev, 2024. "Prediction of Pipe Failure Rate in Heating Networks Using Machine Learning Methods," Energies, MDPI, vol. 17(14), pages 1-16, July.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3511-:d:1437107
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/14/3511/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/14/3511/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shang, Yitong & Li, Sen, 2024. "FedPT-V2G: Security enhanced federated transformer learning for real-time V2G dispatch with non-IID data," Applied Energy, Elsevier, vol. 358(C).
    2. Bo Pang & Erik Nijkamp & Ying Nian Wu, 2020. "Deep Learning With TensorFlow: A Review," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 227-248, April.
    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. Filipe D. Campos & Tiago C. Sousa & Ramiro S. Barbosa, 2024. "Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM," Energies, MDPI, vol. 17(11), pages 1-19, May.
    2. Nitin Kumar Singh & Masaaki Nagahara, 2024. "LightGBM-, SHAP-, and Correlation-Matrix-Heatmap-Based Approaches for Analyzing Household Energy Data: Towards Electricity Self-Sufficient Houses," Energies, MDPI, vol. 17(17), pages 1-32, September.
    3. Yongjie Yang & Yulong Li & Yan Cai & Hui Tang & Peng Xu, 2024. "Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System," Energies, MDPI, vol. 17(15), pages 1-20, July.
    4. Md. Tarek Hasan & Md. Al Emran Hossain & Md. Saddam Hossain Mukta & Arifa Akter & Mohiuddin Ahmed & Salekul Islam, 2023. "A Review on Deep-Learning-Based Cyberbullying Detection," Future Internet, MDPI, vol. 15(5), pages 1-47, May.
    5. Minan Tang & Changyou Wang & Jiandong Qiu & Hanting Li & Xi Guo & Wenxin Sheng, 2024. "Short-Term Load Forecasting of Electric Vehicle Charging Stations Accounting for Multifactor IDBO Hybrid Models," Energies, MDPI, vol. 17(12), pages 1-19, June.
    6. Jabir, Brahim & Moutaouakil, Khalid El & Falih, Noureddine, 2023. "Developing an Efficient System with Mask R-CNN for Agricultural Applications," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 15(1), January.
    7. Francesco Lisi & Ismail Shah, 2024. "Joint Component Estimation for Electricity Price Forecasting Using Functional Models," Energies, MDPI, vol. 17(14), pages 1-18, July.
    8. Xianbin Wang & Yuqi Zhao & Weifeng Li, 2023. "Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model," Sustainability, MDPI, vol. 15(3), pages 1-21, February.
    9. Peng Zhang & Huize Ren & Xiaobin Dong & Xuechao Wang & Mengxue Liu & Ying Zhang & Yufang Zhang & Jiuming Huang & Shuheng Dong & Ruiming Xiao, 2023. "Understanding and Applications of Tensors in Ecosystem Services: A Case Study of the Manas River Basin," Land, MDPI, vol. 12(2), pages 1-23, February.
    10. Wu, Xialai & Lin, Ling & Xie, Lei & Chen, Junghui & Shan, Lu, 2024. "Fast robust optimization of ORC based on an artificial neural network for waste heat recovery," Energy, Elsevier, vol. 301(C).
    11. Afshin Tatar & Amin Shokrollahi & Abbas Zeinijahromi & Manouchehr Haghighi, 2024. "Deep Learning for Predicting Hydrogen Solubility in n-Alkanes: Enhancing Sustainable Energy Systems," Sustainability, MDPI, vol. 16(17), pages 1-24, August.
    12. Zachary K. Collier & Minji Kong & Olushola Soyoye & Kamal Chawla & Ann M. Aviles & Yasser Payne, 2024. "Deep Learning Imputation for Asymmetric and Incomplete Likert-Type Items," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 241-267, April.
    13. Baoyu Fan & Han Ma & Yue Liu & Xiaochen Yuan & Wei Ke, 2024. "KDTM: Multi-Stage Knowledge Distillation Transfer Model for Long-Tailed DGA Detection," Mathematics, MDPI, vol. 12(5), pages 1-19, February.
    14. Vishakha Sood & Reet Kamal Tiwari & Sartajvir Singh & Ravneet Kaur & Bikash Ranjan Parida, 2022. "Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    15. Sichen Shi & Peiyi Wang & Zixuan Zheng & Shu Zhang, 2024. "Two-Layer Optimization Strategy of Electric Vehicle and Air Conditioning Load Considering the Benefit of Peak-to-Valley Smoothing," Sustainability, MDPI, vol. 16(8), pages 1-16, April.

    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:17:y:2024:i:14:p:3511-:d:1437107. 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.