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

X. Wu, X. Hu, S. Moura, X. Yin, and V. Pickert, Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array, Journal of Power Sources, vol.333, pp.203-212, 2016.
DOI : 10.1016/j.jpowsour.2016.09.157

. Coimbra, Solar forecasting methods for renewable energy integration, Progress in Energy and Combustion Science, pp.535-576, 2013.

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. G. Da-silva-fonseca, T. Oozeki, T. Takashima, G. Koshimizu, Y. Uchida 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.
DOI : 10.1017/CBO9780511801389

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

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

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

URL : http://orbit.dtu.dk/files/6262937/poster.pdf

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

H. T. Pedro and C. F. Coimbra, Assessment of forecasting techniques for solar power production with no exogenous inputs, Solar Energy, vol.86, issue.7, pp.2017-2028, 2012.
DOI : 10.1016/j.solener.2012.04.004

C. Monteiro, T. Santos, L. A. Fernandez-jimenez, I. J. Ramirez-rosado, and M. S. Terreros-olarte, Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity, Energies, vol.6, issue.5, p.2624, 2013.
DOI : 10.1214/09-SS054

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

V. G. Berdugo, C. Chaussin, and L. D. , Analog method for collaborative very-short-term forecasting of power generation from photovoltaic systems, p.438

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

Y. Huang, J. Lu, and C. L. , Comparative study of power forecasting methods for PV stations, 2010 International Conference on Power System Technology, 2010.
DOI : 10.1109/POWERCON.2010.5666688

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, E. Garcia-garrido et al., Short-term power forecasting system for photovoltaic plants, Renewable Energy, vol.44, pp.311-317, 2012.
DOI : 10.1016/j.renene.2012.01.108

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

M. Khn, C. Juhlin, H. Held, V. Bruckman, T. Tambach et al., European geosciences union general assembly 2013, egudivision energy, resources & the environment, ere spatio-temporal complementarity between solar and wind power in the iberian peninsula, Energy Procedia, pp.48-57, 2013.

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 : 10.1007/978-3-540-27752-1

J. Tastu, P. Pinson, E. Kotwa, H. Madsen, and H. A. Nielsen, Spatio-temporal analysis and modeling of short-term wind power forecast errors, Wind Energy, vol.127, issue.1, pp.43-60, 2011.
DOI : 10.1007/978-1-4899-4541-9

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

M. He, L. Yang, J. Zhang, and V. Vittal, A Spatio-Temporal Analysis Approach for Short-Term Forecast of Wind Farm Generation, IEEE Transactions on Power Systems, vol.29, issue.4, pp.1611-1622, 2014.
DOI : 10.1109/TPWRS.2014.2299767

J. Tastu, P. Pinson, P. J. Trombe, and H. Madsen, Probabilistic Forecasts of Wind Power Generation Accounting for Geographically Dispersed Information, IEEE Transactions on Smart Grid, vol.5, issue.1, pp.480-489, 2014.
DOI : 10.1109/TSG.2013.2277585

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

J. 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, J. Kleissl, S. Quesada-ruiz, Y. Chu, J. Tovar-pescador et al., Cloud speed impact on solar variability scaling ??? Application to the wavelet variability model, Solar Energy, vol.91, issue.102, pp.11-21, 2013.
DOI : 10.1016/j.solener.2013.01.023

R. Perez, S. Kivalov, J. Schlemmer, K. Hemker-jr, D. Renn et al., Validation of short and medium term operational solar radiation forecasts in the US, Solar Energy, vol.84, issue.12, pp.2161-2172, 2010.
DOI : 10.1016/j.solener.2010.08.014

C. A. Glasbey and D. J. Allcroft, A spatiotemporal auto-regressive moving average model for solar radiation, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.43, issue.3, pp.343-355, 2008.
DOI : 10.1111/1467-9469.00058

D. Yang, C. Gu, Z. Dong, P. Jirutitijaroen, N. Chen et al., Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging, Renewable Energy, vol.60, pp.235-245, 2013.
DOI : 10.1016/j.renene.2013.05.030

A. Tascikaraoglu, B. Sanandaji, G. Chicco, V. Cocina, F. Spertino et al., Compressive Spatio- Temporal Forecasting of Meteorological Quantities and Photovoltaic Power, IEEE Transactions on Sustainable Energy, vol.PP, issue.99, pp.1-1, 2016.
DOI : 10.1109/ptc.2017.7981257

R. Dambreville, P. Blanc, J. Chanussot, and D. Boldo, Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregressive model, Renewable Energy, vol.72, pp.291-300, 2014.
DOI : 10.1016/j.renene.2014.07.012

URL : https://hal.archives-ouvertes.fr/hal-01086935

V. P. Lonij, A. E. Brooks, A. D. Cronin, M. Leuthold, and K. Koch, Intra-hour forecasts of solar power production using measurements from a network of irradiance sensors, Solar Energy, vol.97, pp.58-66, 2013.
DOI : 10.1016/j.solener.2013.08.002

J. D. Patrick, J. L. Harvill, and C. W. Hansen, A semiparametric spatio-temporal model for solar irradiance data, Renewable Energy, vol.87, pp.15-30, 2016.
DOI : 10.1016/j.renene.2015.10.001

URL : http://arxiv.org/pdf/1502.03494

C. Yang, A. A. Thatte, and L. Xie, Multitime-Scale Data-Driven Spatio-Temporal Forecast of Photovoltaic Generation, IEEE Transactions on Sustainable Energy, vol.6, issue.1, pp.104-112, 2015.
DOI : 10.1109/TSTE.2014.2359974

X. G. Agoua, R. Girard, and G. Kariniotakis, Spatio-temporal models for photovoltaic power short-term forecasting Available: https, Solar Integration workshop 2015, 2015.

R. J. Bessa, A. Trindade, C. S. Silva, and V. Miranda, Probabilistic solar power forecasting in smart grids using distributed information, International Journal of Electrical Power & Energy Systems, vol.72, pp.16-23, 2015.
DOI : 10.1016/j.ijepes.2015.02.006

H. C. Hottel, A simple model for estimating the transmittance of direct solar radiation through clear atmospheres, Solar Energy, vol.18, issue.2, pp.129-134, 1976.
DOI : 10.1016/0038-092X(76)90045-1

C. Rigollier, O. Bauer, and L. Wald, On the clear sky model of the ESRA European Solar Radiation
URL : https://hal.archives-ouvertes.fr/hal-00361373