Learning the dynamic of clusters of vertices in a graph for District Heating Network simplification - Archive ouverte HAL
Poster De Conférence Année : 2023

Learning the dynamic of clusters of vertices in a graph for District Heating Network simplification

Apprentissage de la dynamique de clusters de noeuds dans un graphe pour la simplification des réseaux de chaleur urbains

Résumé

District Heating Networks (DHNs) provide very efficient and flexible solutions to produce and supply heat energy for local uses but are computationally expensive both to optimize and simulate. Leveraging the formulation of a DHN as series of graphs with time series signals on its vertices, the objective of this work is to reduce such computational costs by aggregating identified vertices. We investigate recurrent neural network model to learn and mimic the temporal dynamic of the signals of aggregated vertices.
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hal-04639200 , version 1 (08-07-2024)

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  • HAL Id : hal-04639200 , version 1

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Dubon Rodrigue, Mohamed Tahar Mabrouk, Bastien Pasdeloup, Patrick Meyer, Bruno Lacarrière. Learning the dynamic of clusters of vertices in a graph for District Heating Network simplification. Graph Signal Processing workshop 2023, Jun 2023, Oxford, United Kingdom. ⟨hal-04639200⟩
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