3 papers
eess.SY2026
Bayesian Inference for Estimating Generation Costs in Electricity Markets
Matthias Pirlet, Adrien Bolland, Alexandre Huynen +3
Estimating generation costs from observed electricity market data is essential for market simulation, strategic bidding, and system planning. To that end, we model the relationship…
cs.LG2025
Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation
Gérôme Andry, Sacha Lewin, François Rozet +6
Deep learning has advanced weather forecasting, but accurate predictions first require identifying the current state of the atmosphere from observational data. In this work, we int…
cs.LG2024
Cost Estimation in Unit Commitment Problems Using Simulation-Based Inference
Matthias Pirlet, Adrien Bolland, Gilles Louppe +1
The Unit Commitment (UC) problem is a key optimization task in power systems to forecast the generation schedules of power units over a finite time period by minimizing costs while…