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
24 papers · 1 filter
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…
Optimizing Flexibility in Power Systems by Maximizing the Region of Manageable Uncertainties
Aron Zingler, Stephane Fliscounakis, Patrick Panciatici +1
Motivated by the increasing need to hedge against load and generation uncertainty in the operation of power grids, we propose flexibility maximization during operation. We consider…
Implementing transferable annealing protocols for combinatorial optimisation on neutral atom quantum processors: a case study on smart-charging of electric vehicles
Lucas Leclerc, Constantin Dalyac, Pascale Bendotti +3
In the quantum optimization paradigm, variational quantum algorithms face challenges with hardware-specific and instance-dependent parameter tuning, which can lead to computational…
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…
One Wave To Explain Them All: A Unifying Perspective On Feature Attribution
Gabriel Kasmi, Amandine Brunetto, Thomas Fel +1
Feature attribution methods aim to improve the transparency of deep neural networks by identifying the input features that influence a model's decision. Pixel-based heatmaps have b…
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…
Methods for Mitigating Uncertainty in Real-Time Operations of a Connected Microgrid
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran +2
In this paper, we compare the effectiveness of a two-stage control strategy for the energy management system (EMS) of a grid-connected microgrid under uncertain solar irradiance an…
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 Two-Timescale Decision-Hazard-Decision Formulation for Storage Usage Values Calculation
Camila Martinez Parra, Michel de Lara, Jean-Philippe Chancelier +3
The penetration of renewable energies requires additional storages to deal with intermittency. Accordingly, there is growing interest in evaluating the opportunity cost (usage valu…
Evaluation of Performance Measures for Qualifying Flood Models with Satellite Observations
Jean-Paul Travert, Sébastien Boyaval, Cédric Goeury +2
This work discusses how to choose performance measures to compare numerical simulations of a flood event with one satellite image, e.g., in a model calibration or validation proced…
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…
Impact of Generative AI (Large Language Models) on the PRA model construction and maintenance, observations
Valentin Rychkov, Claudia Picoco, Emilie Caleca
The rapid development of Large Language Models (LLMs) and Generative Pre-Trained Transformers(GPTs) in the field of Generative Artificial Intelligence (AI) can significantly impact…
Modeling Common Cause Failure in Dynamic PRA
Claudia Picoco, Valentin Rychkov
In this paper we propose a dynamic model of Common Cause Failures (CCF) that allows to generate common cause events in time. The proposed model is a generalization of Binomial Fail…
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…
Adaptive probabilistic forecasting of French electricity spot prices
Grégoire Dutot, Margaux Zaffran, Olivier Féron +1
Electricity price forecasting (EPF) plays a major role for electricity companies as a fundamental entry for trading decisions or energy management operations. As electricity can no…
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…
Enhanced Electron Extraction in Co-Doped TiO2 Quantified by Drift-Diffusion Simulation for Stable CsPbI3 Solar Cells
Thomas W. Gries, Davide Regaldo, Hans Koebler +22
Solar cells based on inorganic perovskite CsPbI3 are promising candidates to resolve the challenge of operational stability in the field of perovskite photovoltaics. For stable ope…
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…
Analysis of wall-modelled particle/mesh PDF methods for turbulent parietal flows
Guilhem Balvet, Jean-Pierre Minier, Yelva Roustan +1
Lagrangian stochastic methods are widely used to model turbulent flows. Scarce consideration has, however, been devoted to the treatment of the near-wall region and to the formulat…
A Bandit Approach with Evolutionary Operators for Model Selection
Margaux Brégère, Julie Keisler
This work formulates model selection as an infinite-armed bandit problem, namely, a problem in which a decision maker iteratively selects one of an infinite number of fixed choices…
Recommendations for public action towards sustainable generative AI systems
Thomas Le Goff
Growing awareness of the environmental impact of digital technologies has led to several isolated initiatives to promote sustainable practices. However, despite these efforts, the…