C. W. Potter, A. Archambault, and K. Westrick, Building a smarter smart grid through better renewable energy information, 2009 IEEE/PES Power Systems Conference and Exposition, 2009.
DOI : 10.1109/PSCE.2009.4840110

P. Pinson, C. Chevallier, and G. N. Kariniotakis, Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power, IEEE Transactions on Power Systems, vol.22, issue.3, pp.1148-1156, 2007.
DOI : 10.1109/TPWRS.2007.901117

URL : https://hal.archives-ouvertes.fr/hal-00213325

V. Kostylev and A. Pavlovski, Solar power forecasting performances -towards industry standards, Proceedings of 1st International Workshop on the Integration of Solar Power into Power Systems, 2011.

J. Shi, W. Lee, Y. Liu, Y. Yang, and P. Wang, Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines, Industry Applications Society Annual Meeting (IAS), pp.1-6, 2011.
DOI : 10.1109/TIA.2012.2190816

J. Gari, S. Fonseca, T. Oozeki, T. Takashima, G. Koshimizu et al., Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in kitakyushu, Japan, Progress in Photovoltaics: Research and Applications, pp.874-882, 2012.

N. Sharma, P. Sharma, D. Irwin, and P. Shenoy, Predicting solar generation from weather forecasts using machine learning, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2011.
DOI : 10.1109/SmartGridComm.2011.6102379

O. Perpin and E. Lorenzo, Analysis and synthesis of the variability of irradiance and PV power time series with the wavelet transform, Solar Energy, vol.85, issue.1, pp.188-197, 2011.
DOI : 10.1016/j.solener.2010.08.013

M. Zamo, O. Mestre, P. Arbogast, and O. Pannekoucke, A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production, Solar Energy, vol.105, pp.792-803, 2014.
DOI : 10.1016/j.solener.2013.12.006

P. Bacher, H. Madsen, and H. A. Nielsen, Online short-term solar power forecasting, Solar Energy, vol.83, issue.10, pp.1772-1783, 2009.
DOI : 10.1016/j.solener.2009.05.016

P. Bacher, H. Madsen, B. Perers, and H. A. Nielsen, A non-parametric method for correction of global radiation observations, Solar Energy, vol.88, pp.13-22, 2013.
DOI : 10.1016/j.solener.2012.10.024

T. C. Hugo, C. F. Pedro, and . Coimbra, Assessment of forecasting techniques for solar power production with no exogenous inputs, Solar Energy, vol.86, issue.7, pp.2017-2028, 2012.

A. Yona, T. Senjyu, and A. Y. Saber, Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System, 2007 International Conference on Intelligent Systems Applications to Power Systems, 2007.
DOI : 10.1109/ISAP.2007.4441657

H. Yuehui, L. Jing, and C. Liu, Comparative study of power forecasting methods for pv stations, Proceedings of the International Conference on Power System Technology, 2010.

C. Tao, D. Shanxu, and C. Changsong, Forecasting power output for grid-connected photovoltaic power system without using solar radiation measurement, The 2nd International Symposium on Power Electronics for Distributed Generation Systems, 2010.
DOI : 10.1109/PEDG.2010.5545754

L. , A. Fernandez-jimenez, A. Muoz-jimenez, A. Falces, M. Mendoza-villena et al., Zorzano- Santamaria. Short-term power forecasting system for photovoltaic plants, Renewable Energy, vol.44, pp.311-317, 2012.

A. Mellit and A. M. Pavan, A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy, Solar Energy, vol.84, issue.5, pp.807-821, 2010.
DOI : 10.1016/j.solener.2010.02.006

C. Monteiro, T. Santos, L. A. Fernandez-jimenez, I. J. Ramirez-rosado, and M. Sonia-terreros-olarte, Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity, Energies, vol.6, issue.5, p.2624, 2013.
DOI : 10.3390/en6052624

V. Gomez-berdugo, C. Chaussin, and L. Dubus, Analog method for collaborative very-short-term forecasting of power generation from photovoltaic systems

E. Lorenz, D. Heinemann, and C. Kurz, Local and regional photovoltaic power prediction for large scale grid integration: Assessment of a new algorithm for snow detection, Progress in Photovoltaics: Research and Applications, pp.760-769, 2012.
DOI : 10.1002/pip.1224

S. Jerez, R. M. Trigo, A. Sarsa, R. Lorente-plazas, D. Pozo-vzquez et al., Spatio-temporal Complementarity between Solar and Wind Power in the Iberian Peninsula, European Geosciences Union General Assembly 2013, {EGUDivision} Energy, Resources amp; the Environment, pp.48-57, 2013.
DOI : 10.1016/j.egypro.2013.08.007

J. Dowell, S. Weiss, D. Hill, and D. Infield, Short-term spatio-temporal prediction of wind speed and direction, Wind Energy, vol.39, issue.3, pp.1945-1955, 2014.
DOI : 10.1002/we.1682

J. Tastu, P. Pinson, E. Kotwa, H. Madsen, H. Aa et al., Spatio-temporal analysis and modeling of short-term wind power forecast errors, Wind Energy, vol.127, issue.1, pp.43-60, 2011.
DOI : 10.1002/we.401

R. Girard and D. Allard, Spatio-temporal propagation of wind power prediction errors, Wind Energy, vol.97, issue.458, pp.999-1012, 2013.
DOI : 10.1002/we.1527

URL : https://hal.archives-ouvertes.fr/hal-00723820

L. Miao-he, J. Yang, V. Zhang, and . Vittal, A spatio-temporal analysis approach for short-term forecast of wind farm generation. Power Systems, IEEE Transactions on, vol.29, issue.4, pp.1611-1622, 2014.

M. Sherman, Spatial Statistics and Spatio-Temporal Data, 2011.
DOI : 10.1002/9780470974391

J. L. Bosch and J. Kleissl, Cloud motion vectors from a network of ground sensors in a solar power plant, Solar Energy, vol.95, pp.13-20, 2013.
DOI : 10.1016/j.solener.2013.05.027

M. Lave and J. Kleissl, Cloud speed impact on solar variability scaling ??? Application to the wavelet variability model, Solar Energy, vol.91, pp.11-21, 2013.
DOI : 10.1016/j.solener.2013.01.023

S. Quesada-ruiz, Y. Chu, J. Tovar-pescador, H. T. Pedro, and C. F. Coimbra, Cloud-tracking methodology for intra-hour DNI forecasting, Solar Energy, vol.102, pp.267-275, 2014.
DOI : 10.1016/j.solener.2014.01.030

J. C. Zimmerman, Sun-pointing programs and their accuracy, NASA STI/Recon Technical Report N, vol.81, p.30643, 1981.
DOI : 10.2172/6377969

URL : http://www.osti.gov/scitech/servlets/purl/6377969

L. O. Lamm, A new analytic expression for the equation of time, Solar Energy, vol.26, issue.5, p.465, 1981.
DOI : 10.1016/0038-092X(81)90229-2

H. M. Woolf, On the Computation of Solar Evaluation Angles and the Determination of Sunrise and Sunset Times, National Aeronautics and Space Administration Report NASA TM-X, vol.164, 1968.

A. Ea-energy, 50% wind power in Denmark by 2025English summary, Ea Energy Analyses, 2007.