This page has only limited features, please log in for full access.

Unclaimed
Yannig Goude
Électricité de France R&D & Laboratoire de Mathématique d’Orsay, Paris, France

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 31 July 2021 in Applied Energy
Reads 0
Downloads 0

The probabilistic forecasting of electricity loads is crucial for effective scheduling and decision-making in volatile and competitive energy markets with ever-growing uncertainties. We propose a novel approach to construct the probabilistic predictors for curves (PPC) of electricity loads, which leads to properly defined predictive bands and quantiles in the context of curve-to-curve regression. The proposed predictive model provides not only accurate hourly load point forecasts, but also generates well-defined probabilistic bands and day-long trajectories of the loads at any probability level, pre-specified by managers. We also define the predictive quantile curves that exhibit future loads in extreme scenarios and provide insights for hedging risks in the supply management of electricity. When applied to the day-ahead forecasting for French half-hourly electricity loads, the PPC outperform several state-of-the-art time series and machine learning predictive methods with more accurate point forecasts (mean absolute percentage error of 1.10%, compared to 1.36%–4.88% for the alternatives), a higher coverage rate of the day-long trajectory of loads (coverage rate of 95.5%, against 31.9%–90.7% for the alternatives) and a narrower average length of the predictive bands. In a series of numerical experiments, the PPC further demonstrate robust performance and general applicability, achieving accurate coverage probabilities under a variety of data-generating mechanisms.

ACS Style

Xiuqin Xu; Ying Chen; Yannig Goude; Qiwei Yao. Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression. Applied Energy 2021, 301, 117465 .

AMA Style

Xiuqin Xu, Ying Chen, Yannig Goude, Qiwei Yao. Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression. Applied Energy. 2021; 301 ():117465.

Chicago/Turabian Style

Xiuqin Xu; Ying Chen; Yannig Goude; Qiwei Yao. 2021. "Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression." Applied Energy 301, no. : 117465.

Review
Published: 16 April 2021 in Energies
Reads 0
Downloads 0

The field of electric vehicle charging load modelling has been growing rapidly in the last decade. In light of the Paris Agreement, it is crucial to keep encouraging better modelling techniques for successful electric vehicle adoption. Additionally, numerous papers highlight the lack of charging station data available in order to build models that are consistent with reality. In this context, the purpose of this article is threefold. First, to provide the reader with an overview of the open datasets available and ready to be used in order to foster reproducible research in the field. Second, to review electric vehicle charging load models with their strengths and weaknesses. Third, to provide suggestions on matching the models reviewed to six datasets found in this research that have not previously been explored in the literature. The open data search covered more than 860 repositories and yielded around 60 datasets that are relevant for modelling electric vehicle charging load. These datasets include information on charging point locations, historical and real-time charging sessions, traffic counts, travel surveys and registered vehicles. The models reviewed range from statistical characterization to stochastic processes and machine learning and the context of their application is assessed.

ACS Style

Yvenn Amara-Ouali; Yannig Goude; Pascal Massart; Jean-Michel Poggi; Hui Yan. A Review of Electric Vehicle Load Open Data and Models. Energies 2021, 14, 2233 .

AMA Style

Yvenn Amara-Ouali, Yannig Goude, Pascal Massart, Jean-Michel Poggi, Hui Yan. A Review of Electric Vehicle Load Open Data and Models. Energies. 2021; 14 (8):2233.

Chicago/Turabian Style

Yvenn Amara-Ouali; Yannig Goude; Pascal Massart; Jean-Michel Poggi; Hui Yan. 2021. "A Review of Electric Vehicle Load Open Data and Models." Energies 14, no. 8: 2233.

Journal article
Published: 12 June 2020 in International Journal of Forecasting
Reads 0
Downloads 0

Short-Term Load Forecasting (STLF) is a fundamental instrument in the efficient operational management and planning of electric utilities. Emerging smart grid technologies pose new challenges and opportunities. Although load forecasting at the aggregate level has been extensively studied, electrical load forecasting at fine-grained geographical scales of households is more challenging. Among existing approaches, semi-parametric generalized additive models (GAM) have been increasingly popular due to their accuracy, flexibility, and interpretability. Their applicability is justified when forecasting is addressed at higher levels of aggregation, since the aggregated load pattern contains relatively smooth additive components. High resolution data are highly volatile, forecasting the average load using GAM models with smooth components does not provide meaningful information about the future demand. Instead, we need to incorporate irregular and volatile effects to enhance the forecast accuracy. We focus on the analysis of such hybrid additive models applied on smart meters data and show that it leads to improvement of the forecasting performances of classical additive models at low aggregation levels.

ACS Style

Umberto Amato; Anestis Antoniadis; Italia De Feis; Yannig Goude; Audrey Lagache. Forecasting high resolution electricity demand data with additive models including smooth and jagged components. International Journal of Forecasting 2020, 37, 171 -185.

AMA Style

Umberto Amato, Anestis Antoniadis, Italia De Feis, Yannig Goude, Audrey Lagache. Forecasting high resolution electricity demand data with additive models including smooth and jagged components. International Journal of Forecasting. 2020; 37 (1):171-185.

Chicago/Turabian Style

Umberto Amato; Anestis Antoniadis; Italia De Feis; Yannig Goude; Audrey Lagache. 2020. "Forecasting high resolution electricity demand data with additive models including smooth and jagged components." International Journal of Forecasting 37, no. 1: 171-185.

Preprint
Published: 25 October 2019
Reads 0
Downloads 0

While ubiquitous, textual sources of information such as company reports, social media posts, etc. are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, openly accessible daily weather reports from France and the United-Kingdom are leveraged to predict time series of national electricity consumption, average temperature and wind-speed with a single pipeline. Two methods of numerical representation of text are considered, namely traditional Term Frequency - Inverse Document Frequency (TF-IDF) as well as our own neural word embedding. Using exclusively text, we are able to predict the aforementioned time series with sufficient accuracy to be used to replace missing data. Furthermore the proposed word embeddings display geometric properties relating to the behavior of the time series and context similarity between words.

ACS Style

David Obst; Badih Ghattas; Sandra Claudel; Jairo Cugliari; Yannig Goude; Georges Oppenheim. Textual Data for Time Series Forecasting. 2019, 1 .

AMA Style

David Obst, Badih Ghattas, Sandra Claudel, Jairo Cugliari, Yannig Goude, Georges Oppenheim. Textual Data for Time Series Forecasting. . 2019; ():1.

Chicago/Turabian Style

David Obst; Badih Ghattas; Sandra Claudel; Jairo Cugliari; Yannig Goude; Georges Oppenheim. 2019. "Textual Data for Time Series Forecasting." , no. : 1.

Journal article
Published: 02 October 2019 in IEEE Transactions on Smart Grid
Reads 0
Downloads 0

The development of smart grid and new advanced metering infrastructures induces new opportunities and challenges for utilities. Exploiting smart meters information for forecasting stands as a key point for energy providers who have to deal with time varying portfolio of customers as well as grid managers who needs to improve accuracy of local forecasts to face with distributed renewable energy generation development. We propose a new machine learning approach to forecast the system load of a group of customers exploiting individual load measurements in real time and/or exogenous information like weather and survey data. Our approach consists in building experts using random forests trained on some subsets of customers then normalise their predictions and aggregate them with a convex expert aggregation algorithm to forecast the system load. We propose new aggregation methods and compare two strategies for building subsets of customers: 1) hierarchical clustering based on survey data and/or load features and 2) random clustering strategy. These approaches are evaluated on a real data set of residential Irish customers load at a half hourly resolution. We show that our approaches achieve a significant gain in short term load forecasting accuracy of around 25 percent of RMSE.

ACS Style

Benjamin Goehry; Yannig Goude; Pascal Massart; Jean-Michel Poggi. Aggregation of Multi-Scale Experts for Bottom-Up Load Forecasting. IEEE Transactions on Smart Grid 2019, 11, 1895 -1904.

AMA Style

Benjamin Goehry, Yannig Goude, Pascal Massart, Jean-Michel Poggi. Aggregation of Multi-Scale Experts for Bottom-Up Load Forecasting. IEEE Transactions on Smart Grid. 2019; 11 (3):1895-1904.

Chicago/Turabian Style

Benjamin Goehry; Yannig Goude; Pascal Massart; Jean-Michel Poggi. 2019. "Aggregation of Multi-Scale Experts for Bottom-Up Load Forecasting." IEEE Transactions on Smart Grid 11, no. 3: 1895-1904.

Preprint
Published: 27 September 2018
Reads 0
Downloads 0
ACS Style

Matteo Fasiolo; Raphaël Nedellec; Yannig Goude; Simon N. Wood. Scalable visualisation methods for modern Generalized Additive Models. 2018, 1 .

AMA Style

Matteo Fasiolo, Raphaël Nedellec, Yannig Goude, Simon N. Wood. Scalable visualisation methods for modern Generalized Additive Models. . 2018; ():1.

Chicago/Turabian Style

Matteo Fasiolo; Raphaël Nedellec; Yannig Goude; Simon N. Wood. 2018. "Scalable visualisation methods for modern Generalized Additive Models." , no. : 1.

Journal article
Published: 20 July 2018 in Energies
Reads 0
Downloads 0

Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The first section is dedicated to the industrial context and a review of individual electrical data analysis. Then, we focus on hierarchical time-series for bottom-up forecasting. The idea is to decompose the global signal and obtain disaggregated forecasts in such a way that their sum enhances the prediction. This is done in three steps: identify a rather large number of super-consumers by clustering their energy profiles, generate a hierarchy of nested partitions and choose the one that minimize a prediction criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy gives a 16% improvement in forecasting accuracy when applied to French individual consumers. Then, this strategy is implemented using R—the free software environment for statistical computing—so that it can scale when dealing with massive datasets. The proposed solution is to make the algorithm scalable combine data storage, parallel computing and double clustering step to define the super-consumers. The resulting software is openly available.

ACS Style

Benjamin Auder; Jairo Cugliari; Yannig Goude; Jean-Michel Poggi. Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting. Energies 2018, 11, 1893 .

AMA Style

Benjamin Auder, Jairo Cugliari, Yannig Goude, Jean-Michel Poggi. Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting. Energies. 2018; 11 (7):1893.

Chicago/Turabian Style

Benjamin Auder; Jairo Cugliari; Yannig Goude; Jean-Michel Poggi. 2018. "Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting." Energies 11, no. 7: 1893.

Journal article
Published: 22 May 2018 in IEEE Transactions on Knowledge and Data Engineering
Reads 0
Downloads 0

Motivated by the recovery and prediction of electricity consumption time series, we extend Nonnegative Matrix Factorization to take into account external features as side information. We consider general linear measurement settings, and propose a framework which models non-linear relationships between external features and the response variable. We extend previous theoretical results to obtain a sufficient condition on the identifiability of NMF with side information. Based on the classical Hierarchical Alternating Least Squares (HALS) algorithm, we propose a new algorithm (HALSX, or Hierarchical Alternating Least Squares with eXogeneous variables) which estimates NMF in this setting. The algorithm is validated on both simulated and real electricity consumption datasets as well as a recommendation system dataset, to show its performance in matrix recovery and prediction for new rows and columns.

ACS Style

Jiali Mei; Yohann De Castro; Yannig Goude; Jean-Marc Azaïs; Georges Hébrail. Nonnegative Matrix Factorization with Side Information for Time Series Recovery and Prediction. IEEE Transactions on Knowledge and Data Engineering 2018, 31, 493 -506.

AMA Style

Jiali Mei, Yohann De Castro, Yannig Goude, Jean-Marc Azaïs, Georges Hébrail. Nonnegative Matrix Factorization with Side Information for Time Series Recovery and Prediction. IEEE Transactions on Knowledge and Data Engineering. 2018; 31 (3):493-506.

Chicago/Turabian Style

Jiali Mei; Yohann De Castro; Yannig Goude; Jean-Marc Azaïs; Georges Hébrail. 2018. "Nonnegative Matrix Factorization with Side Information for Time Series Recovery and Prediction." IEEE Transactions on Knowledge and Data Engineering 31, no. 3: 493-506.

Preprint
Published: 17 January 2018
Reads 0
Downloads 0

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). We consider general linear measurement settings, and propose a framework which models non-linear relationships between features and the response variables. We extend previous theoretical results to obtain a sufficient condition on the identifiability of the NMF in this setting. Based the classical Hierarchical Alternating Least Squares~(HALS) algorithm, we propose a new algorithm (HALSX, or Hierarchical Alternating Least Squares with eXogeneous variables) which estimates the factorization model. The algorithm is validated on both simulated and real electricity consumption datasets as well as a recommendation dataset, to show its performance in matrix recovery and prediction for new rows and columns.

ACS Style

Jean-Marc Azaïs; Yohann De Castro; Yannig Goude; Georges Hebrail; Jiali Mei. Nonnegative matrix factorization with side information for time series recovery and prediction. 2018, 1 .

AMA Style

Jean-Marc Azaïs, Yohann De Castro, Yannig Goude, Georges Hebrail, Jiali Mei. Nonnegative matrix factorization with side information for time series recovery and prediction. . 2018; ():1.

Chicago/Turabian Style

Jean-Marc Azaïs; Yohann De Castro; Yannig Goude; Georges Hebrail; Jiali Mei. 2018. "Nonnegative matrix factorization with side information for time series recovery and prediction." , no. : 1.

Preprint
Published: 19 September 2017
Reads 0
Downloads 0

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). We consider general linear measurement settings, and propose a framework which models non-linear relationships between features and the response variables. We extend previous theoretical results to obtain a sufficient condition on the identifiability of the NMF in this setting. Based the classical Hierarchical Alternating Least Squares~(HALS) algorithm, we propose a new algorithm (HALSX, or Hierarchical Alternating Least Squares with eXogeneous variables) which estimates the factorization model. The algorithm is validated on both simulated and real electricity consumption datasets as well as a recommendation dataset, to show its performance in matrix recovery and prediction for new rows and columns.

ACS Style

Jiali Mei; Yohann De Castro; Yannig Goude; Jean-Marc Azaïs; Georges Hebrail. Nonnegative matrix factorization with side information for time series recovery and prediction. 2017, 1 .

AMA Style

Jiali Mei, Yohann De Castro, Yannig Goude, Jean-Marc Azaïs, Georges Hebrail. Nonnegative matrix factorization with side information for time series recovery and prediction. . 2017; ():1.

Chicago/Turabian Style

Jiali Mei; Yohann De Castro; Yannig Goude; Jean-Marc Azaïs; Georges Hebrail. 2017. "Nonnegative matrix factorization with side information for time series recovery and prediction." , no. : 1.

Journal article
Published: 01 July 2016 in International Journal of Forecasting
Reads 0
Downloads 0

We summarize the methodology of the team Tololo, which ranked first in the load forecasting and price forecasting tracks of the Global Energy Forecasting Competition 2014. During the competition, we used and tested many different statistical and machine learning methods, such as random forests, gradient boosting machines and generalized additive models. In this paper, we only present the methods that showed the best results. For electric load forecasting, our strategy consists of producing temperature scenarios that we then plug into a probabilistic forecasting load model. Both steps are performed by fitting a quantile generalized additive model (quantGAM). Concerning the electricity price forecasting, we investigate three methods that we used during the competition. The first method follows the spirit of that used for the electric load. The second one is based on combining a set of individual predictors. The last one fits a sparse linear regression to a large set of covariates. We chose to present these three methods in this paper because they perform well and show the potential for improvements in future research.

ACS Style

Pierre Gaillard; Yannig Goude; Raphaël Nedellec. Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting. International Journal of Forecasting 2016, 32, 1038 -1050.

AMA Style

Pierre Gaillard, Yannig Goude, Raphaël Nedellec. Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting. International Journal of Forecasting. 2016; 32 (3):1038-1050.

Chicago/Turabian Style

Pierre Gaillard; Yannig Goude; Raphaël Nedellec. 2016. "Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting." International Journal of Forecasting 32, no. 3: 1038-1050.

Book chapter
Published: 01 January 2015 in Dependence in Probability and Statistics
Reads 0
Downloads 0

Short-term electricity forecasting has been studied for years at EDF and different forecasting models were developed from various fields of statistics or machine learning (functional data analysis, time series, non-parametric regression, boosting, bagging). We are interested in the forecasting of France’s daily electricity load consumption based on these different approaches. We investigate in this empirical study how to use them to improve prediction accuracy. First, we show how combining members of the original set of forecasts can lead to a significant improvement. Second, we explore how to build various and heterogeneous forecasts from these models and analyze how we can aggregate them to get even better predictions.

ACS Style

Pierre Gaillard; Yannig Goude. Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts. Dependence in Probability and Statistics 2015, 95 -115.

AMA Style

Pierre Gaillard, Yannig Goude. Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts. Dependence in Probability and Statistics. 2015; ():95-115.

Chicago/Turabian Style

Pierre Gaillard; Yannig Goude. 2015. "Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts." Dependence in Probability and Statistics , no. : 95-115.

Book chapter
Published: 01 January 2015 in ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers
Reads 0
Downloads 0

The emergence of Smart Grids is posing a wide range of challenges for electric utility companies and network operators: Integration of non-dispatchable power from renewable energy sources (e.g., photovoltaics, hydro and wind), fundamental changes in the way energy is consumed (e.g., due to dynamic pricing, demand response and novel electric appliances), and more active operations of the networks to increase efficiency and reliability. A key in managing these challenges is the ability to forecast network loads at low levels of locality, e.g., counties, cities, or neighbourhoods. Accurate load forecasts improve the efficiency of supply as they help utilities to reduce operating reserves, act more efficiently in the electricity markets, and provide more effective demand-response measures. In order to prepare for the Smart Grid era, there is a need for a scalable simulation environment which allows utilities to develop and validate their forecasting methodology under various what-if-scenarios. This paper presents a massive-scale simulation platform which emulates electrical load in an entire electrical network, from Smart Meters at individual households, over low- to medium-voltage network assets, up to the national level. The platform supports the simulation of changes in the customer portfolio and the consumers’ behavior, installment of new distributed generation capacity at any network level, and dynamic reconfigurations of the network. The paper explains the underlying statistical modeling approach based on Generalized Additive Models, outlines the system architecture, and presents a number of realistic use cases that were generated using this platform.

ACS Style

Pascal Pompey; Alexis Bondu; Yannig Goude; Mathieu Sinn. Massive-Scale Simulation of Electrical Load in Smart Grids Using Generalized Additive Models. ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers 2015, 193 -212.

AMA Style

Pascal Pompey, Alexis Bondu, Yannig Goude, Mathieu Sinn. Massive-Scale Simulation of Electrical Load in Smart Grids Using Generalized Additive Models. ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers. 2015; ():193-212.

Chicago/Turabian Style

Pascal Pompey; Alexis Bondu; Yannig Goude; Mathieu Sinn. 2015. "Massive-Scale Simulation of Electrical Load in Smart Grids Using Generalized Additive Models." ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers , no. : 193-212.

Journal article
Published: 01 April 2014 in International Journal of Forecasting
Reads 0
Downloads 0
ACS Style

Raphael Nedellec; Jairo Cugliari; Yannig Goude. GEFCom2012: Electric load forecasting and backcasting with semi-parametric models. International Journal of Forecasting 2014, 30, 375 -381.

AMA Style

Raphael Nedellec, Jairo Cugliari, Yannig Goude. GEFCom2012: Electric load forecasting and backcasting with semi-parametric models. International Journal of Forecasting. 2014; 30 (2):375-381.

Chicago/Turabian Style

Raphael Nedellec; Jairo Cugliari; Yannig Goude. 2014. "GEFCom2012: Electric load forecasting and backcasting with semi-parametric models." International Journal of Forecasting 30, no. 2: 375-381.

Journal article
Published: 08 August 2012 in Machine Learning
Reads 0
Downloads 0

We consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We first provide a review of the relevant literature and present two theoretical contributions: a general analysis of the specialist aggregation rule of Freund et al. (Proceedings of the Twenty-Ninth Annual ACM Symposium on the Theory of Computing (STOC), pp. 334–343, 1997) and an adaptation of fixed-share rules of Herbster and Warmuth (Mach. Learn. 32:151–178, 1998) in this setting. We then apply these rules to the sequential short-term (one-day-ahead) forecasting of electricity consumption; to do so, we consider two data sets, a Slovakian one and a French one, respectively concerned with hourly and half-hourly predictions. We follow a general methodology to perform the stated empirical studies and detail in particular tuning issues of the learning parameters. The introduced aggregation rules demonstrate an improved accuracy on the data sets at hand; the improvements lie in a reduced mean squared error but also in a more robust behavior with respect to large occasional errors.

ACS Style

Marie Devaine; Pierre Gaillard; Yannig Goude; Gilles Stoltz. Forecasting electricity consumption by aggregating specialized experts. Machine Learning 2012, 90, 231 -260.

AMA Style

Marie Devaine, Pierre Gaillard, Yannig Goude, Gilles Stoltz. Forecasting electricity consumption by aggregating specialized experts. Machine Learning. 2012; 90 (2):231-260.

Chicago/Turabian Style

Marie Devaine; Pierre Gaillard; Yannig Goude; Gilles Stoltz. 2012. "Forecasting electricity consumption by aggregating specialized experts." Machine Learning 90, no. 2: 231-260.