G. N. Bathurst, J. Weatherill, and G. Strbac, Trading wind generation in short term energy markets, IEEE Transactions on Power Systems, vol.17, issue.3, pp.782-789, 2002.
DOI : 10.1109/TPWRS.2002.800950

J. B. Bremnes, Probabilistic wind power forecasts using local quantile regression, Wind Energy, vol.7, issue.1, pp.47-54, 2004.
DOI : 10.1002/we.107

J. B. Bremnes, A comparison of a few statistical models for making quantile wind power forecasts, Wind Energy, vol.39, issue.1-2, pp.3-11, 2006.
DOI : 10.1002/we.182

J. Bröcker and L. A. Smith, From ensemble forecasts to predictive distribution functions. Tellus, Ser. A Dyn, Meteorol. Oceanogr, issue.4, pp.60-663, 2008.

B. G. Brown, R. W. Katz, and A. H. Murphy, Time Series Models to Simulate and Forecast Wind Speed and Wind Power, Journal of Climate and Applied Meteorology, vol.23, issue.8, pp.1184-1195, 1984.
DOI : 10.1175/1520-0450(1984)023<1184:TSMTSA>2.0.CO;2

R. Buizza, P. L. Houtekamer, G. Pellerin, Z. Toth, Y. Zhu et al., A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems, Monthly Weather Review, vol.133, issue.5, pp.1076-1097, 2005.
DOI : 10.1175/MWR2905.1

A. Costa, A. Crespo, J. Navarro, G. Lizcano, H. Madsen et al., A review on the young history of the wind power short-term prediction, Renewable and Sustainable Energy Reviews, vol.12, issue.6, pp.1725-1744, 2008.
DOI : 10.1016/j.rser.2007.01.015

D. Draper, Assessment and propagation of model uncertainty, J. R. Stat. Soc. Ser. B, vol.57, issue.1, pp.45-97, 1995.

A. Fabbri, T. G. San-román, J. R. Abbad, and V. H. Quezada, Assessment of the Cost Associated With Wind Generation Prediction Errors in a Liberalized Electricity Market, IEEE Transactions on Power Systems, vol.20, issue.3, pp.1440-1446, 2005.
DOI : 10.1109/TPWRS.2005.852148

H. R. Glahn and D. Lowry, The Use of Model Output Statistics (MOS) in Objective Weather Forecasting, Journal of Applied Meteorology, vol.11, issue.8, pp.1203-1211, 1972.
DOI : 10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2

T. Gneiting, Quantiles as optimal point forecasts, International Journal of Forecasting, vol.27, issue.2, pp.197-207, 2011.
DOI : 10.1016/j.ijforecast.2009.12.015

T. Gneiting and A. Raftery, Strictly Proper Scoring Rules, Prediction, and Estimation, Journal of the American Statistical Association, vol.102, issue.477, pp.359-378, 2007.
DOI : 10.1198/016214506000001437
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.879

T. Gneiting, A. E. Raftery, A. H. , and T. Goldman, Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Monthly Weather Review, vol.133, issue.5, pp.1098-1118, 2005.
DOI : 10.1175/MWR2904.1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.9212

T. M. Hamill, Interpretation of Rank Histograms for Verifying Ensemble Forecasts, Monthly Weather Review, vol.129, issue.3, pp.550-560, 2001.
DOI : 10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2

T. M. Hamill and S. J. Colucci, Evaluation of Eta???RSM Ensemble Probabilistic Precipitation Forecasts, Monthly Weather Review, vol.126, issue.3, pp.711-724, 1998.
DOI : 10.1175/1520-0493(1998)126<0711:EOEREP>2.0.CO;2

H. Hersbach, Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems. Weather Forecast, pp.559-570, 2000.

J. A. Hoeting, D. Madigan, A. E. Raftery, and C. T. Volinsky, Bayesian Model Averaging: A Tutorial, Stat. Sci, vol.14, issue.4, pp.382-417, 1999.

E. Kalnay, Atmospheric modeling, data assimilation, and predictability, 2003.
DOI : 10.1017/CBO9780511802270
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.88.5725

S. Lerch and T. L. Thorarinsdottir, Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting, Tellus A, vol.1, pp.1-17, 2013.

M. Leutbecher and T. N. Palmer, Ensemble forecasting, Journal of Computational Physics, vol.227, issue.7, pp.3515-3539, 2008.
DOI : 10.1016/j.jcp.2007.02.014

J. M. Lewis, Roots of Ensemble Forecasting, Monthly Weather Review, vol.133, issue.7, pp.1865-1885, 2005.
DOI : 10.1175/MWR2949.1

E. N. Lorenz, Atmospheric predictability experiments with a large numerical model. Tellus A, pp.505-513, 1982.

H. Lund, Large-scale integration of wind power into different energy systems, Energy, vol.30, issue.13, pp.2402-2412, 2005.
DOI : 10.1016/j.energy.2004.11.001

J. M. Morales, A. J. Conejo, H. Madsen, P. Pinson, and M. Zugno, Integrating renewables in electricity markets -Operational problems, of International Series in Operations Research and Management Science, 2014.

P. Pinson and G. Kariniotakis, On-line assessment of prediction risk for wind power production forecasts, Wind Energy, vol.7, issue.2, pp.119-132, 2004.
DOI : 10.1002/we.114

P. Pinson, G. Kariniotakis, H. A. Nielsen, T. S. Nielsen, and H. Madsen, Properties of quantile and interval forecasts of wind generation and their evaluation, Proc. Eur. Wind Energy Conf. Exhib, pp.2-6, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00092435

P. Pinson and H. Madsen, Ensemble-based probabilistic forecasting at Horns Rev, Wind Energy, vol.22, issue.2, pp.137-155, 2009.
DOI : 10.1002/we.309

A. E. Raftery, T. Gneiting, F. Balabdaoui, and M. Polakowski, Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Monthly Weather Review, vol.133, issue.5, pp.1155-1174, 2005.
DOI : 10.1175/MWR2906.1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.9558

M. S. Roulston and L. A. Smith, Combining dynamical and statistical ensembles. Tellus, Ser. A Dyn, Meteorol. Oceanogr, vol.55, issue.1, pp.16-30, 2003.

B. W. Silverman, Density estimation for statistics and data analysis, Monogr. Stat. Appl. Probab, vol.37
DOI : 10.1007/978-1-4899-3324-9

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, issue.489, pp.25-35, 2010.
DOI : 10.1198/jasa.2009.ap08615
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.6436

J. M. Sloughter, T. Gneiting, and A. E. Raftery, Probabilistic Wind Vector Forecasting using Ensembles and Bayesian Model Averaging, Mon. Weather Rev, vol.105, issue.489, p.120718065932001, 2012.

S. S. Soman, H. Zareipour, O. Malik, and P. Mandal, A review of wind power and wind speed forecasting methods with different time horizons. North Am, pp.1-8, 2010.

X. Wang and C. H. Bishop, Improvement of ensemble reliability with a new dressing kernel, Quarterly Journal of the Royal Meteorological Society, vol.83, issue.607, pp.965-986, 2005.
DOI : 10.1256/qj.04.120

G. S. Watson, Smooth regression analysis, Indian J. Stat, vol.26, issue.4, pp.359-372, 1964.

D. Wilks, Comparison of ensemble-MOS methods in the Lorenz '96 setting, Meteorological Applications, vol.17, issue.03, pp.243-256, 2006.
DOI : 10.1017/S1350482706002192

D. S. Wilks, Statistical Methods in the Atmospheric Sciences, Int. Geophys. Ser, vol.100, 2011.

D. S. Wilks and T. M. Hamill, Comparison of Ensemble-MOS Methods Using GFS Reforecasts, Monthly Weather Review, vol.135, issue.6, pp.2379-2390, 2007.
DOI : 10.1175/MWR3402.1

K. Yu and M. C. Jones, Local Linear Quantile Regression, Journal of the American Statistical Association, vol.25, issue.441, pp.228-237, 1998.
DOI : 10.1080/01621459.1998.10474104
URL : http://www.jstor.org/stable/2669619?item_view=download_purchase