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

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.