. .. Intraday, 1.2 Building new forecasts with intraday updates, p.151

. .. Results, 153 7.2.2 Probabilistic forecasts performance and calibration, p.156

. , A Empirical results of quantile-weighted scoring rules with real-world data

A. C. Aitken, On Least Squares and Linear Combination of Observations, Proceedings of the Royal Society of Edinburgh, vol.55, pp.42-48, 1936.

S. Alessandrini, « An analog ensemble for short-term probabilistic solar power forecast, Applied Energy, vol.157, pp.95-110, 2015.

M. P. Almeida, « PV power forecast using a nonparametric PV model, Solar Energy, vol.115, pp.38-092, 2015.

J. L. Anderson, « A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model Integrations, Journal of Climate, vol.9, pp.1518-1530, 1996.

J. Antonanzas, « Review of photovoltaic power forecasting, Solar Energy, vol.136, pp.78-111, 2016.

F. Atger, Estimation of the reliability of ensemble-based probabilistic forecasts, Quarterly Journal of the Royal Meteorological Society, vol.130, pp.627-646, 2004.

J. Audibert, « Fast learning rates in statistical inference through aggregation, The Annals of Statistics, vol.37, pp.1591-1646, 2009.

K. S. Azoury and M. K. Warmuth, Relative loss bounds for on-line density estimation with the exponential family of distributions, Machine Learning, vol.43, pp.211-246, 2001.

P. Bacher, Online short-term solar power forecasting, Solar Energy, vol.83, pp.1772-1783, 2009.

J. Badosa, « Reliability of day-ahead solar irradiance forecasts on Reunion Island depending on synoptic wind and humidity conditions, Solar Energy, vol.115, pp.38-092, 2015.

P. Baudin, Prévision séquentielle par agrégation d'ensemble: application à des prévisions météorologiques assorties d'incertitudes, 2015.

S. Ben-david, Agnostic Online Learning, 2009.

R. Benedetti, « Scoring rules for forecast verification, Monthly Weather Review, vol.138, pp.203-211, 2010.

S. Bentzien and P. Friederichs, Decomposition and graphical portrayal of the quantile score, Quarterly Journal of the Royal Meteorological Society, vol.140, pp.1924-1934, 2014.

J. M. Bernardo, Expected information as expected utility, The Annals of Statistics, pp.686-690, 1979.

G. Biau and B. Patra, Sequential quantile prediction of time series, Information Theory, vol.57, pp.1664-1674, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00606486

P. Blanc, « The HelioClim Project: Surface Solar Irradiance Data for Climate Applications, Remote Sensing, vol.3, pp.2072-4292, 2011.

A. Bossavy, « Forecasting ramps of wind power production with numerical weather prediction ensembles, Wind Energy, vol.16, pp.51-63, 2013.

P. Bougeault, « The THORPEX interactive grand global ensemble, Bulletin of the American Meteorological Society, vol.91, pp.1059-1072, 2010.

N. E. Bowler, Explicitly accounting for observation error in categorical verification of forecasts, Monthly weather review, vol.134, issue.6, pp.1600-1606, 2006.

N. E. Bowler, Accounting for the effect of observation errors on verification of MOGREPS, Meteorological Applications, vol.15, pp.1469-8080, 2008.

L. Breiman, Random forests, Machine learning, vol.45, pp.5-32, 2001.

G. W. Brier, « Verification of Forecasts Expressed in Terms of Probability, Monthly Weather Review, vol.78, pp.1-3, 1950.

J. Bröcker and . Reliability, sufficiency, and the decomposition of proper scores, Quarterly Journal of the Royal Meteorological Society, vol.135, pp.1512-1519, 2009.

J. Bröcker, Evaluating raw ensembles with the continuous ranked probability score, Quarterly Journal of the Royal Meteorological Society, vol.138, pp.1611-1617, 2012.

J. Bröcker and L. A. Smith, Increasing the reliability of reliability diagrams, Weather and forecasting, vol.22, pp.651-661, 2007.

J. Bröcker and L. A. Smith, Scoring probabilistic forecasts: The importance of being proper, Weather and Forecasting, vol.22, pp.382-388, 2007.

R. Buizza, « A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems, Monthly Weather Review, vol.133, pp.1076-1097, 2005.

A. Buja, « Loss functions for binary class probability estimation and classification: Structure and applications, Working draft, 2005.

G. Candille and O. Talagrand, Evaluation of probabilistic prediction systems for a scalar variable, Quarterly Journal of the Royal Meteorological Society, vol.131, pp.1477-870, 2005.

G. Candille and O. Talagrand, Impact of observational error on the validation of ensemble prediction systems, Quarterly Journal of the Royal Meteorological Society, vol.134, pp.1477-870, 2008.

O. Catoni and . Statistical, Learning Theory and Stochastic Optimization, Lectures on Probability Theory and Statistics, Ecole d'été de Probabilités de Saint-Flour XXXI, Lecture Notes in Mathematics, vol.1851, pp.1-269, 2001.

N. Cesa-bianchi and G. Lugosi, « Potential-based algorithms in on-line prediction and game theory, Machine Learning, vol.51, pp.239-261, 2003.

N. Cesa-bianchi and G. Lugosi, Prediction, learning, and games, 2006.

N. Cesa-bianchi, « Improved second-order bounds for prediction with expert advice, International Conference on Computational Learning Theory, pp.217-232, 2005.

N. Cesa-bianchi, Online learning of noisy data, IEEE Transactions on Information Theory, vol.57, pp.7907-7931, 2011.

R. T. Clemen and R. L. Winkler, Combining probability distributions from experts in risk analysis, Risk analysis, vol.19, pp.187-203, 1999.

P. Courtier, « The Arpège project at Météo-France, ECMWF Seminar Proceedings, vol.2, pp.193-231, 1991.

R. Dambreville, « Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregressive model, Renewable Energy, vol.72, pp.291-300, 2014.

A. Dawid and . Comments-on, Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds, TEST 17, vol.2, pp.1133-0686, 2008.

A. Dehghan, « Evaluation and improvement of TAPM in estimating solar irradiance in Eastern Australia, Solar Energy, vol.107, pp.38-092, 2014.

L. Descamps, « PEARP, the Météo-France short-range ensemble prediction system, Quarterly Journal of the Royal Meteorological Society, vol.141, pp.1477-870, 2015.
DOI : 10.1002/qj.2469

M. Devaine, « Forecasting electricity consumption by aggregating specialized experts, Machine Learning, vol.90, pp.885-6125, 2013.
DOI : 10.1007/s10994-012-5314-7
URL : https://link.springer.com/content/pdf/10.1007%2Fs10994-012-5314-7.pdf

W. Ehm, Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings, vol.78, pp.505-562, 2016.
DOI : 10.1111/rssb.12154
URL : http://onlinelibrary.wiley.com/doi/10.1111/rssb.12154/pdf

K. L. Elmore, Alternatives to the chi-square test for evaluating rank histograms from ensemble forecasts, Weather and forecasting, vol.20, pp.789-795, 2005.
DOI : 10.1175/waf884.1

E. S. Epstein, A Scoring System for Probability Forecasts of Ranked Categories, Journal of Applied Meteorology and Climatology, vol.8, pp.985-987, 1969.
DOI : 10.1175/1520-0450(1969)008<0985:assfpf>2.0.co;2
URL : http://journals.ametsoc.org/doi/pdf/10.1175/1520-0450%281969%29008%3C0985%3AASSFPF%3E2.0.CO%3B2

B. Espinar, « Photovoltaic Forecasting: A state of the art, Proceedings 5th European PV-Hybrid and Mini-Grid Conference, 2010.

C. Ferro, Fair scores for ensemble forecasts, Quarterly Journal of the Royal Meteorological Society, vol.140, pp.1917-1923, 2014.
DOI : 10.1002/qj.2270
URL : https://ore.exeter.ac.uk/repository/bitstream/10871/20641/2/Fair%20scores%20for%20ensemble%20forecasts.pdf

C. A. Ferro, On the effect of ensemble size on the discrete and continuous ranked probability scores, Meteorological Applications, vol.15, pp.19-24, 2008.

V. Fortin, « Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?, Journal of Hydrometeorology, vol.15, pp.1708-1713, 2014.

C. Fraley, « Calibrating multimodel forecast ensembles with exchangeable and missing members using Bayesian model averaging, Monthly Weather Review, vol.138, pp.190-202, 2010.
DOI : 10.1175/2009mwr3046.1
URL : http://www.stat.washington.edu/research/reports/2009/tr556.pdf

B. Frénay and M. Verleysen, « Classification in the presence of label noise: a survey, IEEE transactions on neural networks and learning systems, vol.25, pp.845-869, 2014.

T. E. Fricker, « Three recommendations for evaluating climate predictions, Meteorological Applications, vol.20, pp.246-255, 2013.
DOI : 10.1002/met.1409
URL : http://onlinelibrary.wiley.com/doi/10.1002/met.1409/pdf

P. Gaillard, « A Second-order Bound with Excess Losses, Workshop and Conference Proceedings, vol.35, pp.176-196, 2014.

P. Gaillard, « Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting, International Journal of Forecasting, vol.32, pp.1038-1050, 2016.
DOI : 10.1016/j.ijforecast.2015.12.001

C. Gallego-castillo, « A review on the recent history of wind power ramp forecasting, Renewable and Sustainable Energy Reviews, vol.52, pp.1148-1157, 2015.

C. Gautier, « A simple physical model to estimate incident solar radiation at the surface from GOES satellite data, » In: Journal of Applied Meteorology, vol.19, pp.1005-1012, 1980.
DOI : 10.1175/1520-0450(1980)019<1005:aspmte>2.0.co;2

C. Genest and K. J. Mcconway, Allocating the weights in the linear opinion pool, Journal of Forecasting, vol.9, pp.1099-131, 1990.

S. Gerchinovitz, Prediction of individual sequences and prediction in the statistical framework: some links around sparse regression and aggregation techniques, 2011.
URL : https://hal.archives-ouvertes.fr/tel-00653550

J. Geweke and G. Amisano, « Optimal prediction pools, Journal of Econometrics, vol.164, pp.130-141, 2011.
DOI : 10.1016/j.jeconom.2011.02.017
URL : http://www.frbatlanta.org/filelegacydocs/cqerwp_0905.pdf

T. Gneiting, M. Katzfuss, and . Probabilistic-forecasting, Annual Review of Statistics and Its Application, vol.1, pp.125-151, 2014.

T. Gneiting and A. E. Raftery, Strictly proper scoring rules, prediction, and estimation, Journal of the American Statistical Association, vol.102, pp.359-378, 2007.
DOI : 10.21236/ada459827
URL : http://www.dtic.mil/dtic/tr/fulltext/u2/a459827.pdf

T. Gneiting and R. Ranjan, Comparing density forecasts using thresholdand quantile-weighted scoring rules, Journal of Business & Economic Statistics, vol.29, issue.3, 2011.
DOI : 10.1198/jbes.2010.08110
URL : http://www.stat.washington.edu/research/reports/2008/tr533.pdf

T. Gneiting, « Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, vol.133, pp.1098-1118, 2005.
DOI : 10.1175/mwr2904.1
URL : http://www.stat.washington.edu/raftery/Research/PDF/gneiting2005.pdf

L. González-abril, « The similarity between the square of the coefficient of variation and the Gini index of a general random variable, Revista de métodos cuantitativos para la economía y la empresa, vol.10, pp.5-18, 2010.

I. J. Good, « Rational decisions, Journal of the Royal Statistical Society. Series B (Methodological), vol.14, pp.107-114, 1952.

E. P. Grimit, « The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification, Quarterly Journal of the Royal Meteorological Society, vol.132, pp.2925-2942, 2006.
DOI : 10.1256/qj.05.235
URL : https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1256/qj.05.235

R. Hagedorn, « Comparing TIGGE multimodel forecasts with reforecastcalibrated ECMWF ensemble forecasts, Quarterly Journal of the Royal Meteorological Society, vol.138, pp.1477-870, 2012.
DOI : 10.1002/qj.1895

T. M. Hamill, « Interpretation of rank histograms for verifying ensemble forecasts, Monthly Weather Review, vol.129, pp.550-560, 2001.

T. M. Hamill and S. J. Colucci, « Verification of Eta/RSM Short-Range Ensemble Forecasts, Monthly Weather Review, vol.125, pp.1312-1327, 1997.

A. Hammer, « Short-term forecasting of solar radiation: a statistical approach using satellite data, Solar Energy, vol.67, pp.139-150, 1999.

E. Hazan, « Logarithmic regret algorithms for online convex optimization, Machine Learning, vol.69, pp.169-192, 2007.
DOI : 10.1007/s10994-007-5016-8

H. Hersbach, Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather and Forecasting, vol.15, pp.559-570, 2000.

T. E. Hoff, Reporting of irradiance modeling relative prediction errors, Progress in Photovoltaics: Research and Applications, vol.21, pp.1514-1519, 2013.
DOI : 10.1002/pip.2225

J. Huang and M. Perry, « A semi-empirical approach using gradient boosting and-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting, International Journal of Forecasting, pp.169-2070, 2015.
DOI : 10.1016/j.ijforecast.2015.11.002

R. H. Inman, « Solar forecasting methods for renewable energy integration, Progress in Energy and Combustion Science, vol.39, pp.535-576, 2013.
DOI : 10.1016/j.pecs.2013.06.002

I. Jolliffe and D. Stephenson, Forecast Verification: A Practitioner's Guide in Atmospheric Science, p.9781119961079, 2012.

C. Junk, « Analog-Based Ensemble Model Output Statistics, Monthly Weather Review, vol.143, pp.2909-2917, 2015.

R. E. Kalman, A new approach to linear filtering and prediction problems, Journal of basic Engineering, vol.82, pp.35-45, 1960.

J. Kivinen and M. K. Warmuth, Exponentiated Gradient versus Gradient Descent for Linear Predictors, Information and Computation, vol.132, pp.1-63, 1997.

R. Koenker and G. Bassett, « Regression quantiles, Econometrica: journal of the Econometric Society, pp.33-50, 1978.

R. Koenker and K. Hallock, Quantile regression: An introduction, Journal of Economic Perspectives, vol.15, pp.43-56, 2001.

W. M. Koolen, T. Van-erven, and . Second, Quantile Methods for Experts and Combinatorial Games. » In: COLT, vol.40, pp.1155-1175, 2015.

F. Laio and S. Tamea, Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrology and Earth System Sciences Discussions, vol.11, pp.1267-1277, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00298748

P. Lauret, « Solar Forecasting in a Challenging Insular Context, Atmosphere 7, pp.2073-4433, 2016.

S. Lerch, Forecaster's dilemma: Extreme events and forecast evaluation, 2015.

M. Leutbecher, T. N. Palmer, . «-ensemble, and . Forecasting, Journal of Computational Physics, vol.227, pp.3515-3539, 2008.

J. M. Lewis, « Roots of ensemble forecasting, Monthly weather review, vol.133, pp.1865-1885, 2005.

N. Littlestone and M. K. Warmuth, The Weighted Majority Algorithm, Inf. Comput, vol.108, issue.2, pp.890-5401, 1994.

E. Lorenz, « Benchmarking of different approaches to forecast solar irradiance, Proceedings of the 24th European Photovoltaic Solar Energy Conference, pp.4199-4208, 2009.

E. Lorenz, « Irradiance forecasting for the power prediction of gridconnected photovoltaic systems, Selected Topics in Applied Earth Observations and Remote Sensing, vol.2, pp.2-10, 2009.

E. Lorenz, Short term forecasting of solar irradiance by combining satellite data and numerical weather predictions, Proceedings of the 27th European PV Solar Energy Conference, vol.2428, p.44014405, 2012.

H. Luo and R. E. Schapire, Achieving All with No Parameters: AdaNormalHedge, Proceedings of The 28th Conference on Learning Theory, pp.1286-1304, 2015.

V. Mallet, Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation, Journal of Geophysical Research, vol.115, p.24303, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00547903

V. Mallet, Description of Sequential Aggregation Methods and their Performances for Ozone Ensemble Forecasting, 2007.

V. Mallet, « Ozone ensemble forecast with machine learning algorithms, Journal of Geophysical Research, vol.114, p.5307, 2009.

V. Mallet, « Minimax filtering for sequential aggregation: Application to ensemble forecast of ozone analyses, Journal of Geophysical Research, vol.118, issue.11, p.303, 2013.

J. E. Matheson and R. L. Winkler, « Scoring rules for continuous probability distributions, Management science, vol.22, pp.1087-1096, 1976.

H. B. Mcmahan, « Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization, » In: AISTATS, pp.525-533, 2011.

N. Meinshausen, Quantile regression forests, Journal of Machine Learning Research, vol.7, pp.983-999, 2006.

E. C. Merkle and M. Steyvers, Choosing a strictly proper scoring rule, Decision Analysis, vol.10, pp.292-304, 2013.

J. Morcrette, « Radiation and cloud radiative properties in the European Centre for Medium Range Weather Forecasts forecasting system, Journal of Geophysical Research: Atmospheres, vol.96, pp.2156-2202, 1991.

A. H. Murphy, « A Note on the Ranked Probability Score, Journal of Applied Meteorology and Climatology, vol.10, pp.155-156, 1971.

A. H. Murphy, A New Vector Partition of the Probability Score, Journal of Applied Meteorology and Climatology, vol.12, pp.595-600, 1973.

N. Natarajan, « Learning with noisy labels, Advances in neural information processing systems, pp.1196-1204, 2013.

H. A. Nielsen, Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts, Wind Energy 9, vol.1, pp.1099-1824, 2006.

A. Ohmura, Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for climate research, Bulletin of the American Meteorological Society, vol.79, pp.2115-2136, 1998.

F. Orabona, « A generalized online mirror descent with applications to classification and regression, Machine Learning, vol.99, pp.411-435, 2015.

T. Palmer, Stochastic parametrization and model uncertainty. European Centre for Medium-Range Weather Forecasts, 2009.

R. Perez, « Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe ». In: Solar Energy, vol.94, pp.305-326, 2013.

R. Perez, « Comparing satellite remote sensing and ground network measurements for the production of site/time specific irradiance data, Solar Energy, vol.60, issue.2, pp.89-96, 1997.

A. E. Raftery, Using Bayesian model averaging to calibrate forecast ensembles, Monthly Weather Review, vol.133, issue.1, p.174, 2005.

R. Ranjan and T. Gneiting, Combining probability forecasts, Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72.1 (2010), pp.71-91

C. Rao and . Karl, Pearson chi-square test the dawn of statistical inference, Goodness-of-fit tests and model validity, pp.9-24, 2002.

Y. Ren, Ensemble methods for wind and solar power forecasting-A state-of-the-art review, Renewable and Sustainable Energy Reviews, vol.50, pp.1364-0321, 2015.

C. Rigollier, « The method Heliosat-2 for deriving shortwave solar radiation from satellite images, Solar Energy, vol.77, pp.38-092, 2004.

Ø. Saetra, « Effects of Observation Errors on the Statistics for Ensemble Spread and Reliability, Monthly Weather Review, vol.132, pp.1487-1501, 2004.

L. J. Savage, Elicitation of personal probabilities and expectations, Journal of the American Statistical Association, vol.66, pp.783-801, 1971.

M. J. Schervish, A General Method for Comparing Probability Assessors, vol.17, pp.1856-1879, 1989.

M. Scheuerer, Probabilistic quantitative precipitation forecasting using ensemble model output statistics, Quarterly Journal of the Royal Meteorological Society, vol.140, pp.1086-1096, 2014.

Y. Seity, « The AROME-France Convective-Scale Operational Model, Monthly Weather Review, vol.139, pp.976-991, 2011.

S. Shalev-shwartz, Online learning and online convex optimization, Foundations and Trends in Machine Learning, vol.4, pp.107-194, 2011.

E. H. Shuford, Admissible probability measurement procedures, Psychometrika, vol.31, pp.1860-0980, 1966.

J. M. Sloughter, Probabilistic quantitative precipitation forecasting using Bayesian model averaging, Monthly Weather Review, vol.135, pp.3209-3220, 2007.

J. M. Sloughter, Probabilistic wind speed forecasting using ensembles and Bayesian model averaging, Journal of the American Statistical Association 105, vol.489, pp.25-35, 2010.

S. Sperati, Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation, vol.8, pp.9594-9619, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01199212

S. Sperati, « An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting, Solar Energy, vol.133, pp.437-450, 2016.

G. Stoltz, Agrégation séquentielle de prédicteurs : méthodologie générale et applications à la prévision de la qualité de l'air et à celle de la consommation électrique, Journal de la Société Française de Statistique, vol.151, pp.66-106, 2010.

G. J. Szekely and . «-e-statistics, The energy of statistical samples, pp.2000-2003, 2003.

O. Talagrand, Evaluation of Probabilistic Prediction System. Proceedings of the ECMWF Workshop on Predictability, 1999.

J. Thelen and J. M. Edwards, Short-wave radiances: comparison between SEVIRI and the Unified Model, Quarterly Journal of the Royal Meteorological Society, vol.139, pp.1665-1679, 2013.

T. L. Thorarinsdottir and T. Gneiting, Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression, Journal of the Royal Statistical Society: Series A, vol.173, pp.371-388, 2010.

J. Thorey, Ensemble forecast of solar radiation using TIGGE weather forecasts and HelioClim database, Solar Energy, vol.120, pp.232-243, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01184650

J. Thorey, Online learning with the CRPS for ensemble forecasting, Quarterly Journal of the Royal Meteorological Society, 2016.

J. Tödter and B. Ahrens, « Generalization of the Ignorance Score: Continuous Ranked Version and Its Decomposition, Monthly Weather Review, vol.140, pp.2005-2017, 2012.

C. Vernay, « Review of satellite-based surface solar irradiation databases for the engineering, the financing and the operating of photovoltaic systems, ISES Solar World Congress, 2013.

V. Vovk and F. Zhdanov, Prediction With Expert Advice For The Brier Game, J. Mach. Learn. Res, vol.10, pp.1532-4435, 2009.

V. Vovk, Competitive on-line statistics, International Statistical Review/Revue Internationale de Statistique, pp.213-248, 2001.

D. S. Wilks, Extending logistic regression to provide full-probabilitydistribution MOS forecasts, Meteorological Applications, vol.16, pp.1469-8080, 2009.

R. L. Winkler and A. H. Murphy, Good" probability assessors, Journal of applied Meteorology, vol.7, issue.5, pp.751-758, 1968.

O. Wintenberger, Optimal learning with Bernstein online aggregation, Machine Learning, vol.106, pp.119-141, 2017.
URL : https://hal.archives-ouvertes.fr/hal-00973918

Y. Yang, Combining forecasting procedures: some theoretical results, Econometric Theory, vol.20, pp.176-222, 2004.

S. Yitzhaki and E. Schechtman, The Gini Methodology: A primer on a statistical methodology, vol.272, 2012.

T. Yokohata, « Reliability of multi-model and structurally different single-model ensembles, Climate Dynamics, vol.39, pp.930-7575, 2012.

M. Zamo, Post-traitements statistiques de prévisions de vent déterministes et d'ensembles sur une grille, 2016.

M. Zamo, « A benchmark of statistical regression methods for shortterm forecasting of photovoltaic electricity production. Part II: Probabilistic forecast of daily production, Solar Energy 105, pp.804-816, 2014.