3 papers
cs.LG2026
Capturing Multivariate Dependencies of EV Charging Events: From Parametric Copulas to Neural Density Estimation
Martin Výboh, Gabriela Grmanová
Accurate event-based modeling of electric vehicle (EV) charging is essential for grid reliability and smart-charging design. While traditional statistical methods capture marginal…
cs.LG2026
Latent Space Representation of Electricity Market Curves: Maintaining Structural Integrity
Martin Výboh, Zuzana Chladná, Gabriela Grmanová +1
Efficiently representing supply and demand curves is vital for energy market analysis and downstream modelling; however, dimensionality reduction often produces reconstructions tha…
cs.LG2025
Fully Differentiable Lagrangian Convolutional Neural Network for Physics-Informed Precipitation Nowcasting
Peter PavlÃk, Martin Výboh, Anna Bou Ezzeddine +1
This paper presents a convolutional neural network model for precipitation nowcasting that combines data-driven learning with physics-informed domain knowledge. We propose LUPIN, a…