1 code implementation • 9 Jun 2023 • Yvenn Amara-Ouali, Yannig Goude, Nathan Doumèche, Pascal Veyret, Alexis Thomas, Daniel Hebenstreit, Thomas Wedenig, Arthur Satouf, Aymeric Jan, Yannick Deleuze, Paul Berhaut, Sébastien Treguer, Tiphaine Phe-Neau
To fill that gap, the Smarter Mobility Data Challenge has focused on the development of forecasting models to predict EV charging station occupancy.
no code implementations • 24 Jan 2023 • Joseph de Vilmarest, Jethro Browell, Matteo Fasiolo, Yannig Goude, Olivier Wintenberger
The proliferation of local generation, demand response, and electrification of heat and transport are changing the fundamental drivers of electricity load and increasing the complexity of load modelling and forecasting.
2 code implementations • 15 Feb 2022 • Margaux Zaffran, Aymeric Dieuleveut, Olivier Féron, Yannig Goude, Julie Josse
While recent works tackled this issue, we argue that Adaptive Conformal Inference (ACI, Gibbs and Cand{\`e}s, 2021), developed for distribution-shift time series, is a good procedure for time series with general dependency.
no code implementations • 8 Dec 2021 • Yvenn Amara-Ouali, Matteo Fasiolo, Yannig Goude, Hui Yan
In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders of the energy industry.
1 code implementation • 16 Nov 2021 • Anestis Antoniadis, Solenne Gaucher, Yannig Goude
The recent abundance of data on electricity consumption at different scales opens new challenges and highlights the need for new techniques to leverage information present at finer scales in order to improve forecasts at wider scales.
no code implementations • 1 Oct 2021 • Joseph de Vilmarest, Yannig Goude
On the one hand, purely time-series models such as autoregressives are adaptive in essence but fail to capture dependence to exogenous variables.
no code implementations • 18 Feb 2021 • David Obst, Badih Ghattas, Jairo Cugliari, Georges Oppenheim, Sandra Claudel, Yannig Goude
Transfer learning, also referred as knowledge transfer, aims at reusing knowledge from a source dataset to a similar target one.
no code implementations • 14 Sep 2020 • David Obst, Joseph de Vilmarest, Yannig Goude
One of the consequences of this policy is a significant change in electricity consumption patterns.
1 code implementation • 3 Sep 2020 • Xiuqin Xu, Ying Chen, Yannig Goude, Qiwei Yao
When applying to one day ahead forecasting for the French daily electricity load curves, PPC outperform several state-of-the-art predictive methods in terms of forecasting accuracy, coverage rate and average length of the predictive bands.
Methodology Applications
1 code implementation • 20 May 2020 • Christian Capezza, Biagio Palumbo, Yannig Goude, Simon N. Wood, Matteo Fasiolo
We focus on forecasting demand at the individual household level, which is more challenging than forecasting aggregate demand, due to the lower signal-to-noise ratio and to the heterogeneity of consumption patterns across households.
no code implementations • 26 Feb 2020 • Eric Adjakossa, Yannig Goude, Olivier Wintenberger
In this article, we aim at improving the prediction of expert aggregation by using the underlying properties of the models that provide expert predictions.
no code implementations • 25 Oct 2019 • David Obst, Badih Ghattas, Sandra Claudel, Jairo Cugliari, Yannig Goude, Georges Oppenheim
While ubiquitous, textual sources of information such as company reports, social media posts, etc.
no code implementations • 28 Jan 2019 • Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz
We propose a contextual-bandit approach for demand side management by offering price incentives.
no code implementations • 19 Sep 2017 • Jiali Mei, Yohann de Castro, Yannig Goude, Jean-Marc Azaïs, Georges Hébrail
Motivated by the reconstruction and the prediction of electricity consumption, we extend Nonnegative Matrix Factorization~(NMF) to take into account side information (column or row features).
no code implementations • ICML 2017 • Jiali Mei, Yohann de Castro, Yannig Goude, Georges Hébrail
Motivated by electricity consumption reconstitution, we propose a new matrix recovery method using nonnegative matrix factorization (NMF).
no code implementations • 5 Oct 2016 • Jiali Mei, Yohann de Castro, Yannig Goude, Georges Hébrail
Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries.
no code implementations • NeurIPS 2012 • Amadou Ba, Mathieu Sinn, Yannig Goude, Pascal Pompey
In order to quickly track changes in the model and put more weight on recent data, the RLS filter uses a forgetting factor which exponentially weights down observations by the order of their arrival.