Search Results for author: Yannig Goude

Found 17 papers, 5 papers with code

Adaptive Probabilistic Forecasting of Electricity (Net-)Load

no code implementations24 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.

Load Forecasting Uncertainty Quantification

Adaptive Conformal Predictions for Time Series

2 code implementations15 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.

Conformal Prediction Decision Making +4

Daily peak electrical load forecasting with a multi-resolution approach

no code implementations8 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.

Additive models Load Forecasting

Hierarchical transfer learning with applications for electricity load forecasting

1 code implementation16 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.

Additive models Load Forecasting +1

State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting

no code implementations1 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.

BIG-bench Machine Learning Load Forecasting +2

Transfer Learning for Linear Regression: a Statistical Test of Gain

no code implementations18 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.

regression Transfer Learning

Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression

1 code implementation3 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

Additive stacking for disaggregate electricity demand forecasting

1 code implementation20 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.


Kalman Recursions Aggregated Online

no code implementations26 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.

Nonnegative matrix factorization with side information for time series recovery and prediction

no code implementations19 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).

Time Series Time Series Analysis

Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates

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).

Time Series Time Series Analysis

Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates

no code implementations5 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.

Time Series Time Series Analysis

Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting

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.

Additive models Load Forecasting

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