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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poster_gsp-3.pdf (981.98 Ko)
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Conference_GSP_abstract.pdf (350.91 Ko)
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| Licence |
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