425 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR43 papers
- EDF Energy (United Kingdom)GB24 papers
- CERMICSFR17 papers
- Université Paris-SaclayFR17 papers
- Institut de Mathématiques de ToulouseFR16 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR14 papers
- Institut national de recherche en sciences et technologies du numériqueFR14 papers
- Laboratoire de Probabilités, Statistique et ModélisationFR14 papers
- Université Toulouse III - Paul SabatierFR13 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR12 papers
- École PolytechniqueFR12 papers
- Laboratoire de Probabilités et Modèles AléatoiresFR12 papers
10 papers · 2 filters
Self-attentive Transformer for Fast and Accurate Postprocessing of Temperature and Wind Speed Forecasts
Aaron Van Poecke, Tobias Sebastian Finn, Ruoke Meng +6
Current postprocessing techniques often require separate models for each lead time and disregard possible inter-ensemble relationships by either correcting each member separately o…
Improving sub-seasonal wind-speed forecasts in Europe with a non-linear model
Ganglin Tian, Camille Le Coz, Anastase Alexandre Charantonis +3
Sub-seasonal wind speed forecasts provide valuable guidance for wind power system planning and operations, yet the forecast skills of surface winds decrease sharply after two weeks…
Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwaves
Alessandro Lovo, Amaury Lancelin, Corentin Herbert +1
When performing predictions that use Machine Learning (ML), we are mainly interested in performance and interpretability. This generates a natural trade-off, where complex models g…
Automated Spatio-Temporal Weather Modeling for Load Forecasting
Julie Keisler, Margaux Bregere
Electricity is difficult to store, except at prohibitive cost, and therefore the balance between generation and load must be maintained at all times. Electricity is traditionally m…
Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint
Hanyang Wang, Hao Zhou, Sibo Cheng
Despite the success of various methods in addressing the issue of spatial reconstruction of dynamical systems with sparse observations, spatio-temporal prediction for sparse fields…
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…
A Fast Learning-Based Surrogate of Electrical Machines using a Reduced Basis
Alejandro Ribés, Nawfal Benchekroun, Théo Delagnes
A surrogate model approximates the outputs of a solver of Partial Differential Equations (PDEs) with a low computational cost. In this article, we propose a method to build learnin…
MetaCURL: Non-stationary Concave Utility Reinforcement Learning
Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard +1
We explore online learning in episodic loop-free Markov decision processes on non-stationary environments (changing losses and probability transitions). Our focus is on the Concave…
Automated Deep Learning for Load Forecasting
Julie Keisler, Sandra Claudel, Gilles Cabriel +1
Accurate forecasting of electricity consumption is essential to ensure the performance and stability of the grid, especially as the use of renewable energy increases. Forecasting e…
WindDragon: Enhancing wind power forecasting with Automated Deep Learning
Julie Keisler, Etienne Le Naour
Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids. This energy source poses a challenge to system operators…