J. De Vilmarest and Y. Goude, “State-Space Models for Online Post-Covid Electricity Load Forecasting Competition,” in IEEE Open Access Journal of Power and Energy, vol. 9, pp. 192-201, 2022, doi: 10.1109/OAJPE.2022.3141883. https://ieeexplore.ieee.org/document/9677626
Obst, D., De Vilmarest, J., & Goude, Y. (2021). Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France. IEEE Transactions on Power Systems.
Xu, X., , Chen, Y., Goude, Y., Yao, Q. (2021) Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression, accepted in Applied Energies.
Amara-Ouali, Y., Goude, Y., Massart, P., Poggi, J. M., & Yan, H. (2021). A review of electric vehicle load open data and models. Energies, 14(8), 2233.
Amato, U., Antoniadis, A., De Feis, I., Goude, Y., & Lagache, A. (2021). Forecasting high resolution electricity demand data with additive models including smooth and jagged components. International Journal of Forecasting, 37(1), 171-185.
Capezza, C., Palumbo, B., Goude, Y., Wood, S. N., & Fasiolo, M. (2021). Additive stacking for disaggregate electricity demand forecasting. The Annals of Applied Statistics, 15(2), 727-746.
Fasiolo, M., Wood, S. N., Zaffran, M., Nedellec, R., & Goude, Y. (2020). Fast calibrated additive quantile regression. Journal of the American Statistical Association, 1-11.
Fasiolo, M., Nedellec, R., Goude, Y., & Wood, S. N. (2020). Scalable visualization methods for modern generalized additive models. Journal of computational and Graphical Statistics, 29(1), 78-86.
Devijver, E., Goude, Y., & Poggi, J. M. (2020). Clustering electricity consumers using high‐dimensional regression mixture models. Applied Stochastic Models in Business and Industry, 36(1), 159-177.
Goehry, B., Goude, Y., Massart, P., & Poggi, J. M. (2019). Aggregation of Multi-Scale Experts for Bottom-Up Load Forecasting. IEEE Transactions on Smart Grid, 11(3), 1895-1904.
Auder, B.; Cugliari, J.; Goude, Y. & Poggi, J.-M. Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting Energies, 2018, 11.
Mei, J.; Castro, Y. D.; Goude, Y.; Azaïs, J. & Hebrail, G. Nonnegative matrix factorization with side information for time series recovery and prediction IEEE Transactions on Knowledge and Data Engineering, 2018, 1-1.
Amato, U.; Antoniadis, A.; De Feis, I. & Goude, Y. Estimation and group variable selection for additive partial linear models with wavelets and splines South African Statistical Journal, South African Statistical Association (SASA), 2017, 51, 235-272.
Gaillard, P.; Goude, Y. & Nedellec, R. Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting International Journal of Forecasting, Elsevier, 2016, 32, 1038-1050.
Antoniadis, A.; Brossat, X.; Goude, Y.; Poggi, J.-M. & Thouvenot, V. Automatic component selection in additive modeling of french national electricity load forecasting, Nonparametric Statistics, Springer International Publishing, 2016, 191-209.
Thouvenot, V.; Pichavant, A.; Goude, Y.; Antoniadis, A. & Poggi, J. M. Electricity Forecasting Using Multi-Stage Estimators of Nonlinear Additive Models IEEE Transactions on Power Systems, 2016, 31, 3665-3673.
Cho, H.; Goude, Y.; Brossat, X. & Yao, Q. Antoniadis, A.; Poggi, J.-M. & Brossat, X. (Eds.) Modelling and Forecasting Daily Electricity Load via Curve Linear Regression Modeling and Stochastic Learning for Forecasting in High Dimensions, Springer International Publishing, 2015, 217, 35-54.
Gaillard, P. & Goude, Y. Antoniadis, A.; Poggi, J.-M. & Brossat, X. (Eds.) Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts Modeling and Stochastic Learning for Forecasting in High Dimensions, Springer International Publishing, 2015, 217, 95-115.
Pompey, P.; Bondu, A.; Goude, Y. & Sinn, M. Antoniadis, A.; Poggi, J.-M. & Brossat, X. (Eds.) Massive-Scale Simulation of Electrical Load in Smart Grids Using Generalized Additive Models Modeling and Stochastic Learning for Forecasting in High Dimensions, Springer International Publishing, 2015, 217, 193-212.
Wood, S. N.; Goude, Y. & Shaw, S. Generalized additive models for large data sets Journal of the Royal Statistical Society: Series C (Applied Statistics), 2015, 64, 139-155.
Goude, Y.; Nedellec, R. & Kong, N. Local Short and Middle Term Electricity Load Forecasting With Semi-Parametric Additive Models Smart Grid, IEEE Transactions on, 2014, 5, 440-446.
Nedellec, R.; Cugliari, J. & Goude, Y. GEFCom2012: Electric load forecasting and backcasting with semi-parametric models International Journal of Forecasting, 2014, 30, 375 - 381.
Bissuel, C.; Goude, Y. & Pechine, B. Heat load forecasting In J. Taler, editor, Modern Energy Technologies, Systems and Units. Wydawnictwo Politechniki Krakowskiej, Krakow, 2013.
Cho, H.; Goude, Y.; Brossat, X. & Yao, Q. Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach Journal of the American Statistical Association, 2013, 108, 7-21.
Devaine, M.; Gaillard, P.; Goude, Y. & Stoltz, G. Forecasting electricity consumption by aggregating specialized experts - A review of the sequential aggregation of specialized experts, with an application to Slovakian and French country-wide one-day-ahead (half-)hourly predictions Machine Learning, 2013, 90, 231-260.
Enbis workshop Interpretability for Industry 4.0, Pillar 2: Interpretability via additive models 12-13th July 2021, Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdowns in France, from GAM to aggregation of experts, are we still interpretable? slides
Mei, J.; De Castro, Y.; Goude, Y. & Hébrail, G. Precup, D. & Teh, Y. W. (Eds.) Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates Proceedings of the 34th International Conference on Machine Learning, PMLR, 2017, 70, 2382-2390.
Pierrot, A.; Goude, Y. & Yao, Q. Curve Linear Regression with clr The R User Conference, useR! 2017 July 4-7 2017 Brussels, Belgium, 2017, 33.
Mei, J.; Hebrail, G.; Goude, Y. & Kong, N. Spatial Estimation of Electricity Consumption Using Socio-demographic Information APPEEC, IEEE PES APPEEC 2016, 2016.
Cugliari, J.; Goude, Y. & Poggi, J. M. Disaggregated electricity forecasting using wavelet-based clustering of individual consumers 2016 IEEE International Energy Conference (ENERGYCON), 2016, 1-6.
Ba, A.; Sinn, M.; Goude, Y. & Pompey, P. Bartlett, P.; Pereira, F.; Burges, C.; Bottou, L. & Weinberger, K. (Eds.) Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting Advances in Neural Information Processing Systems 25, 2012, 2519-2527
Pierrot, A. & Goude, Y. Short-Term Electricity Load Forecasting With Generalized Additive Models Proceedings of ISAP power, pp 593-600, 2011.
Amara-Ouali, Y., Goude, Y., Massart, P., Poggi, J. M., & Yan, H. (2020). A review of electric vehicle load open data and models.
Obst, D., de Vilmarest, J., & Goude, Y. (2020). Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France. arXiv preprint arXiv:2009.06527.
Xu, X., Chen, Y., Goude, Y., & Yao, Q. (2020). Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression. arXiv preprint arXiv:2009.01595.
Adjakossa, E., Goude, Y., & Wintenberger, O. (2020). Kalman Recursions Aggregated Online. arXiv preprint arXiv:2002.12173.
Obst, D., Ghattas, B., Claudel, S., Cugliari, J., Goude, Y., & Oppenheim, G. (2019). Textual Data for Time Series Forecasting. arXiv preprint arXiv:1910.12618.
Brégère, M., Goude Y., Gaillard, P. and Gilles Stoltz, Target tracking for contextual bandits: Application to demand side management, 2019.
Mei, J.; De Castro, Y.; Goude, Y.; Aza, J.-M. & Hébrail, G. Nonnegative matrix factorization with side information for time series recovery and prediction arXiv preprint arXiv:1709.06320, 2017.
Fasiolo, M.; Goude, Y.; Nedellec, R. & Wood, S. N. Fast calibrated additive quantile regression arXiv preprint arXiv:1707.03307, 2017.
Mei, J., De Castro Y., Goude, Y. and Hebrail, G., Recovering multiple time series from a few temporally aggregated measurements, submitted to ICASSP, 2016.
Devijver, E., Goude, Y. and Poggi, J.-M., Clustering electricity consumers using highdimensional regression mixture models, submitted to Applied Stochastic Models in Business and Industry, 2016.
Antoniadis, A.; Goude, Y.; Poggi, J.-M. & Thouvenot, V. Sélection de variables dans les modèles additifs avec des estimateurs en plusieurs étapes Université d’Orsay ; EDF R&D ; Université Joseph Fourier ; Université Cap Town ; Université Paris Descartes, 2015
GEFCOM14(IEEE Power and Energy Society) Load Forecasting task, rank 1st
GEFCOM12(IEEE Power and Energy Society) Price Forecasting task, rank 1st
GEFCOM12(IEEE Power and Energy Society) Load Forecasting task, rank 3rd
2015-2016 IIF Tao Hong Award: Pierre Gaillard, Yannig Goude and Raphael Nedellec (2016) Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting”. International Journal of Forecasting 32(3): 1038-1050.
With Joseph Moullart de Vilmarest, we arrived firs in the competition Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm: https://ieee-dataport.org/competitions/day-ahead-electricity-demand-forecasting-post-covid-paradigm.