Leak localization in District Heating Networks integrating physical model-based and data driven-based methods: Impact of dataset construction on model performance

Guang Yang, Dinghuang Xing and Hai Wang

Energy, 2024, vol. 308, issue C

Abstract: Leaks in district heating networks result in waste of water and heat, and even threaten personal safety. Various machine learning-based methods have been proposed to realize leak localization and gain satisfying performance based on a large amount of leak samples. However, the creation of such samples demands a substantial investment of time and computational resources, presenting challenges for real-time applications. Previous studies have rarely addressed the influence of leak dataset construction and efficient creation methods. To fill this limited area, this paper conducts a comparative analysis on the variables in leak dataset and proposes an effective method for sample dataset generation, complemented by a novel accuracy evaluation that incorporates the topological relationship among pipelines. A multi-source looped heating network is employed to do the case study. The results demonstrate that the proposed method achieves satisfactory leak localization performance with a significantly reduced training set. Furthermore, by segmenting the physical pipeline into multiple virtual pipelines and using the adjacent evaluation metric, the leak localization method achieves a 75 m error with an accuracy of 0.998.

Keywords: Leak localization; Dataset construction; Artificial neural networks; District Heating Networks (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224026136
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:308:y:2024:i:c:s0360544224026136

DOI: 10.1016/j.energy.2024.132839

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().