5 papers
Spatio-temporal modelling of electric vehicle charging demand
Kaoutar Bouaachra, Yvenn Amara-Ouali, Yannig Goude +1
Accurate forecasting of electric vehicle (EV) charging demand is critical for grid management and infrastructure planning. Yet the field continues to rely on legacy benchmarks; suc…
Cascaded Transfer: Learning Many Tasks under Budget Constraints
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +2
In distributed applications, such as energy demand forecasting at the substation level or federated learning, a large number of related tasks must be learned by different models, w…
Graph Neural Networks for Electricity Load Forecasting
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +2
Forecasting electricity demand is increasingly challenging as energy systems become more decentralized and intertwined with renewable sources. Graph Neural Networks (GNNs) have rec…
Conformal Prediction for Hierarchical Data
Guillaume Principato, Gilles Stoltz, Yvenn Amara-Ouali +3
We consider conformal prediction for multivariate data and focus on hierarchical data, where some components are linear combinations of others. Intuitively, the hierarchical struct…
Leveraging Graph Neural Networks to Forecast Electricity Consumption
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +1
Accurate electricity demand forecasting is essential for several reasons, especially as the integration of renewable energy sources and the transition to a decentralized network pa…