Abstract:
Utility network is a commonly used term for a collection of physical infrastructure components such as pipes, valves, pumps, etc., pipes that supply utilities like heat, water, electricity, and gas throughout the city. Structurally, the network of interconnected components can be replicated digitally with the help of GIS measurements. An accurate digital representation is crucial to maintain data integrity and ensure operational reliability. However, when physical components are digitally represented, measurement inaccuracies are introduced, diminishing the reliability of the digital model and impeding the process of deriving meaningful information. These inaccuracies often appear as missing pipes or disconnected networks due to translational errors. This thesis aims to formulate and tackle this problem as a graph based link (edge) prediction task using Deep Learning (DL), through Graph Neural Networks (GNNs). We apply the theory and methodology of SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction) [1], to the domain of utility networks. SEAL extracts local enclosing subgraphs around a link to learn patterns and predicts the existence of links. Additionally, we compare the effect of using pre-trained node2vec embeddings to embeddings learned simultaneously with the GNN model while experimenting with two different graph structures – homogeneous and heterogeneous-bipartite representations. We applied the methodology to real-world heat and water networks from Denmark. Overall, the pre-trained node2vec embeddings consistently outperformed those simultaneously learned with the GNN model. The optimal choice for the graph structure varied between the heat and water networks. Our experimentation on the heat network shows that heterogeneous-bipartite representation yielded better results, with an AUC score of 98% on the test set. In the case of the water networks, both the heterogeneous bipartite and the homogeneous representations produced comparable results, with an AUC score of 95%.
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This thesis is part of the “UtilityTwin” project.