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

Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Oleg Evgenievich Babikov and Iliya Krastev Iliev ()
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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

Energies, 2024, vol. 17, issue 14, 1-16

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.

Keywords: machine learning; heating network; evaluation of the value feature; evaluation of heat supply reliability; intelligent model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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