S. Alessandrini, L. Delle-monache, S. Sperati, and G. Cervone, An analog ensemble for short-term probabilistic solar power forecast, Applied Energy, vol.157, pp.95-110, 2015.

M. P. Almeida, O. Perpiñán, and L. Narvarte, PV power forecast using a nonparametric PV model, Solar Energy, vol.115, pp.354-368, 2015.

J. L. Anderson, A method for producing and evaluating probabilistic forecasts from ensemble model integrations 9, pp.1518-1530, 1996.

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

P. Bacher, H. Madsen, and H. A. Nielsen, Online short-term solar power forecasting, Solar Energy, vol.83, pp.1772-1783, 2009.

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

G. W. Brier, Verification of forecasts expressed in terms of probability, vol.78, pp.1-3, 1950.

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.

G. Candille and O. Talagrand, Evaluation of probabilistic prediction systems for a scalar variable, Quarterly Journal of the Royal Meteorological Society, vol.131, pp.2131-2150, 2005.

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, Y. Mansour, and G. Stoltz, Improved second-order bounds for prediction with expert advice, International Conference on Computational Learning Theory, pp.217-232, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00019799

P. Courtier, C. Freydier, J. Geleyn, F. Rabier, and M. Rochas, The Arpège project at Météo-France, ECMWF Seminar Proceedings, pp.193-231, 1991.

L. Descamps, C. Labadie, A. Joly, E. Bazile, P. Arbogast et al., PEARP, the Météo-France short-range ensemble prediction system, Quarterly Journal of the Royal Meteorological Society, vol.141, pp.1671-1685, 2015.

M. Devaine, P. Gaillard, Y. Goude, and G. Stoltz, Forecasting electricity consumption by aggregating specialized experts, Machine Learning, vol.90, pp.231-260, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00484940

V. Fortin, M. Abaza, F. Anctil, and R. Turcotte, Why should ensemble spread match the rmse of the ensemble mean, Journal of Hydrometeorology, vol.15, pp.1708-1713, 2014.

P. Gaillard, Y. Goude, and R. Nedellec, Additive models and robust aggregation for gefcom2014 probabilistic electric load and electricity price forecasting, International Journal of Forecasting, vol.32, pp.1038-1050, 2016.

P. Gaillard, G. Stoltz, and T. Van-erven, A second-order bound with excess losses, Proceedings of COLT'14, JMLR: Workshop and Conference Proceedings, pp.176-196, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00943665

C. Genest and K. J. Mcconway, Allocating the weights in the linear opinion pool, Journal of Forecasting, vol.9, pp.53-73, 1990.

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 : https://www.econstor.eu/bitstream/10419/153451/1/ecbwp1017.pdf

T. Gneiting and M. Katzfuss, 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, A. E. Raftery, I. Westveld, A. H. Goldman, and T. , 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

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

T. M. Hamill and S. J. Colucci, Verification of Eta/RSM short-range ensemble forecasts 125, pp.1312-1327, 1997.
DOI : 10.1175/1520-0493(1997)125<1312:voersr>2.0.co;2

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

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, 2015.
DOI : 10.1016/j.ijforecast.2015.11.002

R. H. Inman, H. T. Pedro, and C. F. Coimbra, 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, 2012.

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

W. M. Koolen and T. Van-erven, Second-order quantile methods for experts and combinatorial games, pp.1155-1175, 2015.

E. Lorenz, J. Hurka, D. Heinemann, and H. G. Beyer, Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.2, pp.2-10, 2009.

H. Luo and R. E. Schapire, Achieving all with no parameters: Adanormalhedge, Proceedings of The 28th Conference on Learning Theory, COLT 2015, pp.1286-1304, 2015.

V. ;. Mallet, G. Stoltz, and B. Mauricette, Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation, J. Geophys. Res. 115. Mallet, vol.114, 2009.
DOI : 10.1029/2010jd014259
URL : https://hal.archives-ouvertes.fr/inria-00547903

H. A. Nielsen, H. Madsen, and T. S. Nielsen, Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts, Wind Energy, vol.9, pp.95-108, 2006.
DOI : 10.1002/we.180
URL : http://orbit.dtu.dk/en/publications/using-quantile-regression-to-extend-an-existing-wind-power-forecasting-system-with-probabilistic-forecasts(31731d8f-02ed-4687-a7c7-c32399e314ce).html

T. Palmer, R. Buizza, F. Doblas-reyes, T. Jung, M. Leutbecher et al., Stochastic parametrization and model uncertainty, 2009.

A. E. Raftery, T. Gneiting, F. Balabdaoui, and M. Polakowski, Using Bayesian model averaging to calibrate forecast ensembles 133, vol.1, p.174, 2005.
DOI : 10.21236/ada459828
URL : http://www.stat.washington.edu/raftery/Research/PDF/fadoua2005.pdf

Y. Ren, P. Suganthan, and N. Srikanth, Ensemble methods for wind and solar power forecasting-a state-of-the-art review, Renewable and Sustainable Energy Reviews, vol.50, pp.82-91, 2015.

J. M. Sloughter, T. Gneiting, and A. E. Raftery, Probabilistic wind speed forecasting using ensembles and bayesian model averaging, Journal of the American Statistical Association, vol.105, pp.25-35, 2010.
DOI : 10.21236/ada488100
URL : http://www.dtic.mil/dtic/tr/fulltext/u2/a488100.pdf

J. M. Sloughter, A. E. Raftery, T. Gneiting, and C. Fraley, Probabilistic quantitative precipitation forecasting using bayesian model averaging, Monthly Weather Review, vol.135, pp.3209-3220, 2007.

S. Sperati, S. Alessandrini, P. Pinson, and G. Kariniotakis, The "weather intelligence for renewable energies" 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

G. Stoltz, Agrégation séquentielle de prédicteurs : méthodologie générale et applicationsàapplicationsà la prévision de la qualité de l'air etàetà celle de la consommationélectriqueconsommationélectrique 151, pp.66-106, 2010.

O. Talagrand, R. Vautard, and B. Strauss, Evaluation of probabilistic prediction system, Proceedings of the ECMWF Workshop on Predictability, 1999.

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 (Statistics in Society, vol.173, pp.371-388, 2010.

J. Thorey, V. Mallet, and P. Baudin, Online learning with the CRPS for ensemble forecasting, Quarterly Journal of the Royal Meteorological Society, 2016.

J. Thorey, V. Mallet, C. Chaussin, L. Descamps, and P. Blanc, 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

D. S. Wilks, Extending logistic regression to provide full-probability-distribution MOS forecasts, Meteorological Applications, vol.16, pp.361-368, 2009.

O. Wintenberger, Optimal learning with bernstein online aggregation, Machine Learning, vol.106, pp.119-141, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01380175

M. Zamo, O. Mestre, P. Arbogast, and O. Pannekoucke, A benchmark of statistical regression methods for shortterm forecasting of photovoltaic electricity production. part ii: Probabilistic forecast of daily production, Solar Energy, vol.105, pp.804-816, 2014.