J. Kleissl, Solar energy forecasting and resource assessment, 2013.

H. Rich, . Inman, T. C. Hugo, C. F. Pedro, and . Coimbra, Solar forecasting methods for renewable energy integration, Progress in Energy and Combustion Science, vol.39, issue.6, pp.535-576, 2013.

, International energy agency. World energy statistics

, United Nations. Convention-cadre sur les changements climatiques

, International energy agency. Renewables energy statistics

, LOI n 2015-992 du 17 août 2015 relative à la transition énergétique pour la croissance verte, 2015.

, Direction générale de l'énergie et du climat. Programmation pluriannuelle de l'énergie période, 2009.

C. W. Potter, A. Archambault, and K. Westrick, Building a Smarter Smart Grid Through Better Renewable Energy Information, Proceedings of Power Systems Conference and Exposition, 2009.

P. Pinson, C. Chevallier, and G. N. Kariniotakis, Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power. Power Systems, IEEE Transactions on, vol.22, issue.3, pp.1148-1156, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00617685

G. Kariniotakis, Renewable Energy Forecasting : From Models to Applications, p.6, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01542722

J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-pison et al., Review of photovoltaic power forecasting. Solar Energy, vol.136, pp.78-111, 2016.

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.

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

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. Energies, vol.8, pp.9594-9619, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01199212

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.

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

F. G. Shuman, Numerical Weather Prediction, Bulletin of the American Meteorological Society, vol.59, issue.1, pp.5-17, 1978.

S. Pelland, G. Galanis, and G. Kallos, Solar and photovoltaic forecasting through post-processing of the Global Environmental Multiscale numerical weather prediction model, Progress in Photovoltaics : Research and Applications, vol.21, issue.3, pp.284-296, 2013.

R. Perez, S. Kivalov, J. Schlemmer, K. Hemker, D. Renné 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.

R. Perez, K. Moore, S. Wilcox, D. Renné, and A. Zelenka, Forecasting solar radiation-Preliminary evaluation of an approach based upon the national forecast database, Solar Energy, vol.81, issue.6, pp.809-812, 2007.

P. Ineichen, Comparison of eight clear sky broadband models against 16 independent data banks, Solar Energy, vol.80, issue.4, pp.468-478, 2006.

C. Rigollier, O. Bauer, and L. Wald, On the clear sky model of the esra-european solar radiation atlas-with respect to the heliosat method, Solar Energy, vol.68, issue.1, pp.33-48, 2000.
URL : https://hal.archives-ouvertes.fr/hal-00361373

R. E. Bird and R. L. Hulstrom, Simplified clear sky model for direct and diffuse insolation horizontal surfaces, 1981.

Y. H. Benjamin, R. C. Liu, and . Jordan, The interrelationship and characteristic distribution of direct, diffuse and total solar radiation, Solar Energy, vol.4, pp.1-19, 1960.

J. N. Black, C. W. Bonython, and J. A. Prescott, Solar radiation and the duration of sunshine, Quarterly Journal of the Royal Meteorological Society, vol.80, issue.344, pp.231-235, 1954.

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/126769008/Manuscript.pdf

N. A. Engerer and F. P. Mills, KPV : A clear-sky index for photovoltaics, Solar Energy, vol.105, pp.679-693, 2014.
DOI : 10.1016/j.solener.2014.04.019

J. Hamilton, Time series analysis, vol.2, 1994.

E. Lorenz, J. Hurka, D. Heinemann, and H. G. Beyer, Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.2, issue.1, pp.2-10, 2009.

P. Bacher and H. Madsen, Bengt Perers, and Henrik Aalborg Nielsen. A nonparametric method for correction of global radiation observations, Solar Energy, vol.88, pp.13-22, 2013.

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

URL : https://www.mdpi.com/1996-1073/6/5/2624/pdf

V. Berdugo, C. Chaussin, L. Dubus, G. Hebrail, and V. Leboucher, Analog method for collaborative very-short-term forecasting of power generation from photovoltaic systems, Next Generation Data Mining Summit (NGDM'11), 2011.

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, vol.20, pp.760-769, 2012.

M. Pierro, F. Bucci, M. D. Felice, E. Maggioni, D. Moser et al., Multi-Model Ensemble for day ahead prediction of photovoltaic power generation, Solar Energy, vol.134, pp.132-146, 2016.

M. G. De-giorgi, P. M. Congedo, and M. Malvoni, Photovoltaic power forecasting using statistical methods : impact of weather data, IET Science, Measurement Technology, vol.8, issue.3, pp.90-97, 2014.

S. Haykin, Neural Networks : A Comprehensive Foundation, 1998.

A. Yona, T. Senjyu, A. Y. Saber, T. Funabashi, H. Sekine et al., Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System, International Conference on Intelligent Systems Applications to Power Systems, pp.1-6, 2007.

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, pp.773-777, 2010.
DOI : 10.1109/pedg.2010.5545754

A. Dolara, F. Grimaccia, S. Leva, M. Mussetta, and E. Ogliari, A physical hybrid artificial neural network for short term forecasting of pv plant power output, Energies, vol.8, issue.2, 2015.

Y. Huang, J. Lu, C. Liu, X. Xu, W. Wang et al., Comparative study of power forecasting methods for PV stations, 2010 International Conference on Power System Technology (POWERCON), pp.1-6, 2010.

L. , A. Fernandez-jimenez, A. Muñoz-jimenez, A. Falces, M. Mendoza-villena 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.

A. Mellit, A. Massi-pavan, and V. Lughi, Short-term forecasting of power production in a large-scale photovoltaic plant, Solar Energy, vol.105, pp.401-413, 2014.

I. Stylianos, E. G. Vagropoulos, C. K. Kardakos, A. G. Simoglou, J. P. Bakirtzis et al., ANN-based scenario generation methodology for stochastic variables of electric power systems, Electric Power Systems Research, vol.134, pp.9-18, 2016.

N. Cristianini and J. Shawe-taylor, An Introduction to Support Vector Machines : And Other Kernel-based Learning Methods, 2000.

J. Gari-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, vol.20, issue.7, pp.874-882, 2012.

N. Sharma, P. Sharma, D. Irwin, and P. Shenoy, Predicting Solar Generation from Weather Forecasts Using Machine Learning, Proceedings of IEEE International Conference on Smart Grid Communications, 2011.

O. Perpiñán 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.

J. Shi, W. Lee, Y. Liu, Y. Yang, and P. Wang, Forecasting power output of photovoltaic system based on weather classification and support vector machine, 2011 IEEE Industry Applications Society Annual Meeting (IAS), pp.1-6, 2011.

M. De-felice, M. Petitta, and P. M. Ruti, Short-term predictability of photovoltaic production over Italy, vol.80, pp.197-204, 2015.

M. Bouzerdoum, A. Mellit, and A. Massi-pavan, A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant, Solar Energy, vol.98, pp.226-235, 2013.

J. Li, J. K. Ward, J. Tong, L. Collins, and G. Platt, Machine learning for solar irradiance forecasting of photovoltaic system, vol.90, pp.542-553, 2016.

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, vol.32, issue.3, pp.1081-1086, 2016.

Q. Yang, D. An, and Y. Cai, A Novel Evolution Kalman Filter Algorithm for Short-Term Climate Prediction, Asian Journal of Control, vol.18, issue.1, pp.400-405, 2016.

R. Koenker, Quantile Regression, 2005.

J. W. Taylor and J. Jeon, Forecasting wind power quantiles using conditional kernel estimation, vol.80, pp.370-379, 2015.

F. Golestaneh, P. Pinson, and H. B. Gooi, Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation #x2014 ; With Application to Solar Energy, IEEE Transactions on Power Systems, vol.31, issue.5, pp.3850-3863, 2016.

E. Scolari, F. Sossan, and M. Paolone, Irradiance prediction intervals for PV stochastic generation in microgrid applications, Solar Energy, vol.139, pp.116-129, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02066049

Y. He, Q. Xu, J. Wan, and S. Yang, Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function, Energy, vol.114, pp.498-512, 2016.

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.

I. Gábor, G. Nagy, S. Barta, G. Kazi, G. Borbély et al., GEFCom2014 : Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach, International Journal of Forecasting, vol.32, issue.3, pp.1087-1093, 2016.

Y. Chu and C. F. Coimbra, Short-term probabilistic forecasts for Direct Normal Irradiance, vol.101, pp.526-536, 2017.

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

Y. Liu, S. Shimada, J. Yoshino, T. Kobayashi, Y. Miwa et al., Ensemble forecasting of solar irradiance by applying a mesoscale meteorological model, Solar Energy, vol.136, pp.597-605, 2016.

M. David, F. Ramahatana, P. J. Trombe, and P. Lauret, Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models, Solar Energy, vol.133, pp.55-72, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01310208

A. Grantham, Y. R. Gel, and J. Boland, Nonparametric short-term probabilistic forecasting for solar radiation, Solar Energy, vol.133, pp.465-475, 2016.
DOI : 10.1016/j.solener.2016.04.011

J. Zhang, B. Hodge, and A. Florita, Joint Probability Distribution and Correlation Analysis of Wind and Solar Power Forecast Errors in the Western Interconnection, Journal of Energy Engineering, vol.141, issue.1, p.4014008, 2015.

G. Tina and S. Gagliano, Probabilistic analysis of weather data for a hybrid solar/wind energy system, International Journal of Energy Research, vol.35, issue.3, pp.221-232, 2011.

M. J. Sanjari and H. B. Gooi, Probabilistic Forecast of PV Power Generation Based on Higher-Order Markov Chain, IEEE Transactions on Power Systems, issue.99, pp.1-1, 2016.

E. B. Iversen, J. M. Morales, J. K. Møller, and H. Madsen, Shortterm probabilistic forecasting of wind speed using stochastic differential equations, International Journal of Forecasting, vol.32, issue.3, pp.981-990, 2016.

E. B. Iversen, J. M. Morales, J. K. Møller, and H. Madsen, Probabilistic Forecasts of Solar Irradiance by Stochastic Differential Equations, 2013.

H. Cihan, P. Dagli, S. Mandal, . Teja-swarroop, A. Madhira et al., Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques, Procedia Computer Science, vol.12, pp.332-337, 2012.

A. Bracale, P. Caramia, G. Carpinelli, A. R. Di-fazio, and P. Varilone, A BayesianBased Approach for a Short-Term Steady-State Forecast of a Smart Grid, IEEE Transactions on Smart Grid, vol.4, issue.4, pp.1760-1771, 2013.

A. Bracale, P. Caramia, and G. Carpinelli, A Bayesian-Based Approach for a Short-Term Steady-State Forecast of a Smart Grid, Proceedings of IEEE TRANSACTIONS ON SMART GRID, 2009.

J. Dowell, S. Weiss, D. Hill, and D. Infield, Short-term spatiotemporal prediction of wind speed and direction, Wind Energy, vol.17, issue.12, pp.1945-1955, 2014.

R. Girard and D. Allard, Spatio-temporal propagation of wind power prediction errors, Wind Energy, vol.16, issue.7, pp.999-1012, 2013.
DOI : 10.1002/we.1527

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

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.14, issue.1, pp.43-60, 2011.
DOI : 10.1002/we.401

URL : http://orbit.dtu.dk/files/6380046/tastuetal09_rev.pdf

S. Jerez, R. M. Trigo, A. Sarsa, R. Lorente-plazas, D. Pozo-vázquez et al., Spatio-temporal Complementarity between Solar and Wind Power in the Iberian Peninsula, Energy Procedia, vol.40, pp.48-57, 2013.

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

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.

A. Tascikaraoglu, Evaluation of spatio-temporal forecasting methods in various smart city applications, Renewable and Sustainable Energy Reviews, vol.82, pp.424-435, 2018.

M. Journée and C. Bertrand, Improving the spatio-temporal distribution of surface solar radiation data by merging ground and satellite measurements, Remote Sensing of Environment, vol.114, issue.11, pp.2692-2704, 2010.

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.

M. Lave and J. Kleissl, Cloud speed impact on solar variability scalingApplication to the wavelet variability model, Solar Energy, vol.91, pp.11-21, 2013.

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.2-10, 2014.

E. Lorenz, J. Kühnert, D. Heinemann, K. P. Nielsen, J. Remund et al., Comparison of global horizontal irradiance forecasts based on numerical weather prediction models with different spatio-temporal resolutions, Progress in Photovoltaics : Research and Applications, vol.24, issue.12, pp.1626-1640, 2016.

R. Dambreville, P. Blanc, J. Chanussot, and D. Boldo, Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregressive model, vol.72, pp.291-300, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01086935

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.57, issue.3, pp.343-355, 2008.

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.

M. Lazzaroni, S. Ferrari, V. Piuri, A. Salman, L. Cristaldi et al., Models for solar radiation prediction based on different measurement sites, Measurement, vol.63, pp.346-363, 2015.

J. D. Patrick, J. L. Harvill, and C. W. Hansen, A semiparametric spatio-temporal model for solar irradiance data, vol.87, pp.15-30, 2016.

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, issue.99, pp.1-1, 2016.

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.

P. A. Vincent, A. E. Lonij, A. D. Brooks, M. Cronin, K. Leuthold et al., Intra-hour forecasts of solar power production using measurements from a network of irradiance sensors, Solar Energy, vol.97, pp.58-66, 2013.

C. Yang and L. Xie, A novel ARX-based multi-scale spatio-temporal solar power forecast model, 2012 North American Power Symposium (NAPS), pp.1-6, 2012.

F. Golestaneh, P. Hoay-beng-gooi, and . Pinson, Generation and evaluation of space-time trajectories of photovoltaic power, Applied Energy, vol.176, pp.80-91, 2016.

J. R. Andrade and R. J. Bessa, Improving Renewable Energy Forecasting with a Grid of Numerical Weather Predictions, IEEE Transactions on Sustainable Energy, issue.99, pp.1-1, 2017.

R. J. Bessa, A. Trindade, S. P. Cátia, V. Silva, and . Miranda, Probabilistic solar power forecasting in smart grids using distributed information, International Journal of Electrical Power & Energy Systems, vol.72, pp.16-23, 2015.

T. E. Hoff and R. Perez, Quantifying PV power Output Variability, Solar Energy, vol.84, issue.10, pp.1782-1793, 2010.

C. Hoyt and . Hottel, A simple model for estimating the transmittance of direct solar radiation through clear atmospheres. Solar Energy, vol.18, pp.129-134, 1976.

D. A. Dickey, D. P. Hasza, and W. A. Fuller, Testing for Unit Roots in Seasonal Time Series, Journal of the American Statistical Association, vol.79, issue.386, pp.355-367, 1984.

E. Said, D. A. Said, and . Dickey, Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order, Biometrika, vol.71, issue.3, pp.599-607, 1984.

P. A. Moran, The Interpretation of Statistical Maps, Journal of the Royal Statistical Society. Series B (Methodological), vol.10, issue.2, pp.243-251, 1948.

W. D. Koenig, Spatial autocorrelation of ecological phenomena, Trends in Ecology & Evolution, vol.14, issue.1, pp.22-26, 1999.

L. Breiman, Random Forests, Machine Learning, vol.45, pp.5-32, 2001.

Y. Lin and Y. Jeon, Random Forests and Adaptive Nearest Neighbors, Journal of the American Statistical Association, vol.101, issue.474, pp.578-590, 2006.

R. Tibshirani, Regression Shrinkage and Selection Via the Lasso, Journal of the Royal Statistical Society, Series B, vol.58, pp.267-288, 1994.

. R-core-team, R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2014.

S. Ferrari, M. Lazzaroni, V. Piuri, L. Cristaldi, and M. Faifer, Statistical models approach for solar radiation prediction, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp.1734-1739, 2013.

J. Zhang, A. Florita, B. Hodge, S. Lu, H. F. Hamann et al., A suite of metrics for assessing the performance of solar power forecasting, Solar Energy, vol.111, pp.157-175, 2015.

S. Mekhilef, R. Saidur, and M. Kamalisarvestani, Effect of dust, humidity and air velocity on efficiency of photovoltaic cells, Renewable and Sustainable Energy Reviews, vol.16, issue.5, pp.2920-2925, 2012.

E. Skoplaki and J. A. Palyvos, On the temperature dependence of photovoltaic module electrical performance : A review of efficiency/power correlations, Solar Energy, vol.83, issue.5, pp.614-624, 2009.

, ECMWF. Medium-range forecasts, 2017.

. Ecmwf, Atmospheric model high resolution 10-day forecast (hres, 2017.

P. Bei-zhang, M. Dehghanian, and . Kezunovic, Spatial-temporal solar power forecast through use of Gaussian Conditional Random Fields, IEEE Power and Energy Society General Meeting (PESGM), pp.1-5, 2016.

Y. Zhang and J. Wang, K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting, International Journal of Forecasting, vol.32, issue.3, pp.1074-1080, 2016.

B. W. Silverman, Density Estimation for Statistics and Data Analysis, 1986.

G. R. Terrell and D. W. Scott, Variable Kernel Density Estimation, The Annals of Statistics, vol.20, issue.3, pp.1236-1265, 1992.

M. P. Wand and M. C. Jones, Kernel Smoothing, 1994.

D. W. Scott, Multivariate Density Estimation : Theory, Practice, and Visualization, Google-Books-ID : XZ03BwAAQBAJ, 2015.

A. Mugdadi and I. Ahmad, A bandwidth selection for kernel density estimation of functions of random variables, Computational Statistics & Data Analysis, vol.47, issue.1, pp.49-62, 2004.

A. Travis, K. O'brien, N. R. Kashinath, W. D. Cavanaugh, J. P. Collins et al., A fast and objective multidimensional kernel density estimation method : fastKDE, Computational Statistics & Data Analysis, vol.101, pp.148-160, 2016.

T. Duong and M. L. Hazelton, Cross-validation Bandwidth Matrices for Multivariate Kernel Density Estimation, Scandinavian Journal of Statistics, vol.32, issue.3, pp.485-506, 2005.

D. Xavier and G. Pauline, La régression quantile en pratique. Document de travail INSEE, 2013.

T. M. Cover and J. A. Thomas, Elements of information theory, 1991.

J. Juban, N. Siebert, and G. N. Kariniotakis, Probabilistic Short-term Wind Power Forecasting for the Optimal Management of Wind Generation, IEEE Lausanne Power Tech, pp.683-688, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00222776

. R-core-team, R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2014.

H. Hersbach, Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather and Forecasting, vol.15, issue.5, pp.559-570, 2000.

M. Noia, C. F. Ratto, and R. Festa, Solar irradiance estimation from geostationary satellite data : I. Statistical models. Solar Energy, vol.51, pp.449-456, 1993.

M. Noia, C. F. Ratto, and R. Festa, Solar irradiance estimation from geostationary satellite data : II. Physical models. Solar Energy, vol.51, pp.457-465, 1993.

R. Perez, P. Ineichen, K. Moore, M. Kmiecik, C. Chain et al., A new operational model for satellite-derived irradiances : description and validation, Solar Energy, vol.73, issue.5, pp.307-317, 2002.

A. Hammer, D. Heinemann, C. Hoyer, R. Kuhlemann, E. Lorenz et al., Solar energy assessment using remote sensing technologies, Remote Sensing of Environment, vol.86, issue.3, pp.423-432, 2003.

C. Rigollier, L. Lefèvre, and . Wald, The method Heliosat-2 for deriving shortwave solar radiation from satellite images, Solar Energy, vol.77, issue.2, pp.159-169, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00361364

V. Badescu, C. A. Gueymard, S. Cheval, C. Oprea, and M. Baciu, Computing global and diffuse solar hourly irradiation on clear sky. Review and testing of 54 models, Alexandru Dumitrescu, Flavius Iacobescu, Ioan Milos, and Costel Rada, vol.16, pp.1636-1656, 2012.

, Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets, Solar Energy, vol.132, pp.25-37, 2016.

P. Blanc, J. Remund, and L. Vallance, Short-term solar power forecasting based on satellite images, pp.179-198, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01542855

Z. Peng, D. Yu, D. Huang, J. Heiser, and P. Kalb, A hybrid approach to estimate the complex motions of clouds in sky images, Solar Energy, vol.138, pp.10-25, 2016.

Z. Dong, D. Yang, T. Reindl, and W. M. Walsh, Satellite image analysis and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics, Energy Conversion and Management, vol.79, pp.66-73, 2014.

S. Cros, O. Liandrat, N. Sébastien, and N. Schmutz, Extracting cloud motion vectors from satellite images for solar power forecasting, 2014 IEEE Geoscience and Remote Sensing Symposium, pp.4123-4126, 2014.

H. Escrig, F. J. Batlles, J. Alonso, F. M. Baena, J. L. Bosch et al., Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast, Energy, vol.55, pp.853-859, 2013.

A. Linares-rodriguez, S. Quesada-ruiz, D. Pozo-vazquez, and J. Tovar-pescador, An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery, Energy, pp.264-273, 2015.

C. Voyant, P. Haurant, M. Muselli, C. Paoli, and M. Nivet, Time series modeling and large scale global solar radiation forecasting from geostationary satellites data, Solar Energy, vol.102, pp.131-142, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00932955

R. Marquez, T. C. Hugo, C. F. Pedro, and . Coimbra, Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to, ANNs. Solar Energy, pp.176-188, 2013.

H. S. Jang, K. Y. Bae, H. S. Park, and D. K. Sung, Solar Power Prediction Based on Satellite Images and Support Vector Machine, IEEE Transactions on Sustainable Energy, vol.7, issue.3, pp.1255-1263, 2016.

B. Wolff, J. Kühnert, E. Lorenz, O. Kramer, and D. Heinemann, Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data, Solar Energy, vol.135, pp.197-208, 2016.

L. M. Aguiar, B. Pereira, M. David, F. Díaz, and P. Lauret, Use of satellite data to improve solar radiation forecasting with Bayesian Artificial Neural Networks, Solar Energy, vol.122, pp.1309-1324, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01232089

L. M. Aguiar, B. Pereira, P. Lauret, F. Díaz, and M. David, Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting, Renewable Energy, pp.599-610, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01334564

P. Blanc, B. Gschwind, M. Lefèvre, and L. Wald, The HelioClim Project : Surface Solar Irradiance Data for Climate Applications, Remote Sensing, vol.3, issue.2, pp.343-361, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00566995

L. Wald, M. Albuisson, C. Best, C. Delamare, D. Dumortier et al., SoDa : a Web service on solar radiation, PSE GmbH, vol.3, pp.3-9809656, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00465551

C. Vernay, P. Blanc, and S. Pitaval, Characterizing measurements campaigns for an innovative calibration approach of the global horizontal irradiation estimated by HelioClim-3. Renewable Energy, vol.57, pp.339-347, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00799172

C. Thomas, L. Saboret, E. Wey, P. Blanc, and L. Wald, Preliminary assessment of a new SoDa service for real-time estimates and shortterm forecasts of the solar radiation, 15th EMS Annual Meeting / 12th ECAM, USKKey, p.335, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01202638

B. Gschwind and L. Wald, HC-1+HC-3" a long-term data set of daily solar radiation at surface, EMS Annual Meeting, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01583336

R. Dambreville, Nowcasting and very short term forecasting of the global horizontal irradiance at ground level : application to photovoltaic output forecasting, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01130251

D. Wartenberg, Multivariate Spatial Correlation : A Method for Exploratory Geographical Analysis, Geographical Analysis, vol.17, issue.4, pp.263-283, 1985.

F. Glineur, Etude des méthodes de point intérieur appliquées à la programmation linéaire et à la programmation semidéfinie

G. B. Dantzig, Linear Programming and Extensions, 1991.

R. Koenker and J. A. Machado, Goodness of Fit and Related Inference Processes for Quantile Regression, Journal of the American Statistical Association, vol.94, issue.448, pp.1296-1310, 1999.

C. W. Potter, A. Archambault, and K. Westrick, Building a smarter smart grid through better renewable energy information, Proceedings of Power Systems Conference and Exposition, 2009.

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.
URL : https://hal.archives-ouvertes.fr/hal-00617685

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.

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, issue.6, 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 system based on weather classification and support vector machine, Industry Applications Society Annual Meeting (IAS), pp.1-6, 2011.

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, Progress in Photovoltaics: Research and Applications, vol.20, pp.874-882, 2012.

N. Sharma, P. Sharma, D. Irwin, and P. Shenoy, Predicting solar generation from weather forecasts using machine learning, Proceedings of IEEE International Conference on Smart Grid Communications, 2011.

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.

M. Zamo, O. Mestre, P. Arbogast, and O. Pannekoucke, A benchmark of statistical regression methods for shortterm forecasting of photovoltaic electricity production, part i: Deterministic forecast of hourly production, Solar Energy, vol.105, pp.792-803, 2014.

P. Bacher, H. Madsen, and H. A. Nielsen, Online shortterm solar power forecasting, Solar Energy, vol.83, issue.10, pp.1772-1783, 2009.

P. Bacher, H. Madsen, B. Perers, and H. A. Nielsen, A nonparametric method for correction of global radiation observations, Solar Energy, vol.88, pp.13-22, 2013.

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.

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.

V. G. Berdugo, C. Chaussin, and L. D. , 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, vol.20, pp.760-769, 2012.

Y. A. , S. T. , and S. A. , Application of neural network to one-dayahead 24 hours generating power forecasting for photovoltaic system, Proceedings of the International Conference on Intelligent Systems Applications to Power Systems, 2007.

Y. Huang, J. Lu, and C. L. , 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, Proceedings of the IEEE International Symposium on Power Electronics for Distributed Generation Systems, 2010.

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.

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.

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, vol.40, pp.48-57, 2013.

J. Dowell, S. Weiss, D. Hill, and D. Infield, Short-term spatiotemporal prediction of wind speed and direction, Wind Energy, vol.17, issue.12, pp.1945-1955, 2014.

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.14, issue.1, pp.43-60, 2011.

,

R. Girard and D. Allard, Spatio-temporal propagation of wind power prediction errors, Wind Energy, vol.16, issue.7, pp.999-1012, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00723820

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.

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.

M. Sherman, Spatial Statistics and Spatio-Temporal Data, 2011.

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.

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.

S. Quesada-ruiz, Y. Chu, J. Tovar-pescador, H. Pedro, and C. Coimbra, Cloud-tracking methodology for intra-hour {DNI} forecasting, Solar Energy, vol.102, pp.267-275, 2014.

R. Perez, S. Kivalov, J. Schlemmer, K. Hemker, 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.

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.57, issue.3, pp.343-355, 2008.

D. Yang, C. Gu, Z. Dong, P. Jirutitijaroen, N. Chen et al., Solar irradiance forecasting using spatialtemporal covariance structures and time-forward kriging, Renewable Energy, vol.60, pp.235-245, 2013.

A. Tascikaraoglu, B. Sanandaji, G. Chicco, V. Cocina, F. Spertino et al., Compressive SpatioTemporal Forecasting of Meteorological Quantities and Photovoltaic Power, IEEE Transactions on Sustainable Energy, issue.99, pp.1-1, 2016.

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.
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.

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.

C. Yang, A. A. Thatte, and L. Xie, Multitime-Scale Data-Driven SpatioTemporal Forecast of Photovoltaic Generation, IEEE Transactions on Sustainable Energy, vol.6, issue.1, pp.104-112, 2015.

X. G. Agoua, R. Girard, and G. Kariniotakis, Spatio-temporal models for photovoltaic power short-term forecasting, Solar Integration workshop 2015, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01220321

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.

H. C. Hottel, A simple model for estimating the transmittance of direct solar radiation through clear atmospheres, Solar Energy, vol.18, pp.129-134, 1976.

C. Rigollier, O. Bauer, and L. Wald,

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, pp.1-5, 2009.

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.
URL : https://hal.archives-ouvertes.fr/hal-00617685

G. Kariniotakis, Renewable Energy Forecasting: From Models to Applications, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01542722

J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-pison et al., Review of photovoltaic power forecasting, Solar Energy, vol.136, pp.78-111, 2016.

S. Sobri, S. Koohi-kamali, and N. A. Rahim, Solar photovoltaic generation forecasting methods: A review, Energy Conversion and Management, vol.156, pp.459-497, 2018.

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.

S. Sperati, S. Alessandrini, and L. Delle-monache, An application of the ecmwf ensemble prediction system for short-term solar power forecasting, Solar Energy, vol.133, pp.437-450, 2016.

M. Pierro, F. Bucci, M. De-felice, E. Maggioni, D. Moser et al., Multi-Model Ensemble for day ahead prediction of photovoltaic power generation, Solar Energy, vol.134, pp.132-146, 2016.

Y. Liu, S. Shimada, J. Yoshino, T. Kobayashi, Y. Miwa et al., Ensemble forecasting of solar irradiance by applying a mesoscale meteorological model, Solar Energy, vol.136, pp.597-605, 2016.

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

G. I. Nagy, G. Barta, S. Kazi, G. Borbély, and G. Simon, GEFCom2014: Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach, International Journal of Forecasting, vol.32, issue.3, pp.1087-1093, 2016.

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, vol.32, issue.3, pp.1081-1086, 2016.

E. Scolari, F. Sossan, and M. Paolone, Irradiance prediction intervals for PV stochastic generation in microgrid applications, Solar Energy, vol.139, pp.116-129, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02066049

Y. Chu and C. F. Coimbra, Short-term probabilistic forecasts for Direct Normal Irradiance, Renewable Energy, vol.101, pp.526-536, 2017.

A. Yona, T. Senjyu, A. Y. Saber, T. Funabashi, H. Sekine et al., 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, pp.1-6, 2007.

Y. Huang, J. Lu, C. Liu, X. Xu, W. Wang et al., Comparative study of power forecasting methods for PV stations, 2010 International Conference on Power System Technology, pp.1-6, 2010.

A. Mellit and A. 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.

A. Dolara, F. Grimaccia, S. Leva, M. Mussetta, and E. Ogliari, A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output, Energies, vol.8, issue.2, pp.1138-1153, 2015.

H. Hassani and E. S. Silva, Forecasting with Big Data: A Review, Annals of Data Science, vol.2, issue.1, pp.5-19, 2015.

S. I. Vagropoulos, E. G. Kardakos, C. K. Simoglou, A. G. Bakirtzis, and J. P. Catalão, ANN-based scenario generation methodology for stochastic variables of electric power systems, Electric Power Systems Research, vol.134, pp.9-18, 2016.

C. Y. Zhang, C. L. Chen, M. Gan, and L. Chen, Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting, IEEE Transactions on Sustainable Energy, vol.6, issue.4, pp.1416-1425, 2015.

M. Khodayar, O. Kaynak, and M. E. Khodayar, Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting, IEEE Transactions on Industrial Informatics, vol.13, issue.6, pp.2770-2779, 2017.

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.57, issue.3, pp.343-355, 2008.

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

R. Amaro, M. C. Silva, and . Brito, Impact of network layout and time resolution on spatio-temporal solar forecasting, Solar Energy, vol.163, pp.329-337, 2018.

M. Journée and C. Bertrand, Improving the spatio-temporal distribution of surface solar radiation data by merging ground and satellite measurements, Remote Sensing of Environment, vol.114, issue.11, pp.2692-2704, 2010.

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.

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.

M. Lazzaroni, S. Ferrari, V. Piuri, A. Salman, L. Cristaldi et al., Models for solar radiation prediction based on different measurement sites, Measurement, vol.63, pp.346-363, 2015.

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, vol.20, pp.760-769, 2012.

E. Lorenz, J. Kühnert, D. Heinemann, K. P. Nielsen, J. Remund et al., Comparison of global horizontal irradiance forecasts based on numerical weather prediction models with different spatio-temporal resolutions, Progress in Photovoltaics: Research and Applications, vol.24, issue.12, pp.1626-1640, 2016.

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.

F. Gutierrez-corea, M. Manso-callejo, M. Moreno-regidor, and M. Manrique-sancho, Forecasting short-term solar irradiance based on artificial neural networks and data from neighboring meteorological stations, Solar Energy, vol.134, pp.119-131, 2016.

Y. He, Q. Xu, J. Wan, and S. Yang, Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function, Energy, vol.114, pp.498-512, 2016.

J. Li, J. K. Ward, J. Tong, L. Collins, and G. Platt, Machine learning for solar irradiance forecasting of photovoltaic system, Renewable Energy, vol.90, pp.542-553, 2016.

A. Tascikaraoglu, B. Sanandaji, G. Chicco, V. Cocina, F. Spertino et al., Compressive SpatioTemporal Forecasting of Meteorological Quantities and Photovoltaic Power, IEEE Transactions on Sustainable Energy, issue.99, pp.1-1, 2016.

C. Yang, A. A. Thatte, and L. Xie, Multitime-Scale Data-Driven SpatioTemporal Forecast of Photovoltaic Generation, IEEE Transactions on Sustainable Energy, vol.6, issue.1, pp.104-112, 2015.

C. Yang and L. Xie, A novel ARX-based multi-scale spatio-temporal solar power forecast model, 2012 North American Power Symposium (NAPS), pp.1-6, 2012.

V. Berdugo, C. Chaussin, L. Dubus, G. Hebrail, and V. Leboucher, Analog method for collaborative very-short-term forecasting of power generation from photovoltaic systems, Next Generation Data Mining Summit (NGDM'11), 2011.
URL : https://hal.archives-ouvertes.fr/hal-02278607

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.

F. Golestaneh, H. B. Gooi, and P. Pinson, Generation and evaluation of space-time trajectories of photovoltaic power, Applied Energy, vol.176, pp.80-91, 2016.

B. Zhang, P. Dehghanian, and M. Kezunovic, Spatial-temporal solar power forecast through use of Gaussian Conditional Random Fields, 2016 IEEE Power and Energy Society General Meeting (PESGM), pp.1-5, 2016.

X. G. Agoua, R. Girard, and G. Kariniotakis, Short-term spatiotemporal forecasting of photovoltaic power production, IEEE Transactions on Sustainable Energy, issue.99, pp.1-1, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01581946

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.

A. Tascikaraoglu, B. M. Sanandaji, G. Chicco, V. Cocina, F. Spertino et al., A short-term spatiotemporal approach for Photovoltaic power forecasting, 2016 Power Systems Computation Conference (PSCC), pp.1-7, 2016.

F. Golestaneh, P. Pinson, and H. B. Gooi, Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation #x2014; With Application to Solar Energy, IEEE Transactions on Power Systems, vol.31, issue.5, pp.3850-3863, 2016.

Y. Zhang and J. Wang, K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting, International Journal of Forecasting, vol.32, issue.3, pp.1074-1080, 2016.

P. Bacher, H. Madsen, and H. A. Nielsen, Online short-term solar power forecasting, Solar Energy, vol.83, issue.10, pp.1772-1783, 2009.

B. W. Silverman, Density Estimation for Statistics and Data Analysis, 1986.

G. R. Terrell and D. W. Scott, Variable Kernel Density Estimation, The Annals of Statistics, vol.20, issue.3, pp.1236-1265, 1992.

M. P. Wand and M. C. Jones, Kernel Smoothing, 1994.

D. W. Scott, Multivariate Density Estimation: Theory, Practice, and Visualization, 2015.

A. R. Mugdadi and I. A. Ahmad, A bandwidth selection for kernel density estimation of functions of random variables, Computational Statistics & Data Analysis, vol.47, issue.1, pp.49-62, 2004.

T. A. O'brien, K. Kashinath, N. R. Cavanaugh, W. D. Collins, and J. P. O'brien, A fast and objective multidimensional kernel density estimation method: fastKDE, Computational Statistics & Data Analysis, vol.101, pp.148-160, 2016.

T. Duong and M. L. Hazelton, Cross-validation Bandwidth Matrices for Multivariate Kernel Density Estimation, Scandinavian Journal of Statistics, vol.32, issue.3, pp.485-506, 2005.

T. M. Cover and J. A. Thomas, Elements of information theory, 1991.

R. Koenker, Quantile Regression, 2005.

G. B. Dantzig, Linear Programming and Extensions, 2007.

R. Tibshirani, Regression Shrinkage and Selection Via the Lasso, Journal of the Royal Statistical Society, Series B, vol.58, pp.267-288, 1994.
DOI : 10.1111/j.2517-6161.1996.tb02080.x

. R-core-team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, 2014.

P. Pinson, H. A. Nielsen, J. K. Møller, H. Madsen, and G. N. Kariniotakis, Non-parametric probabilistic forecasts of wind power: required properties and evaluation, Wind Energy, vol.10, issue.6, pp.497-516, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00525361

T. Gneiting and A. E. Raftery, Strictly Proper Scoring Rules, Prediction, and Estimation, Journal of the American Statistical Association, vol.102, issue.477, pp.359-378, 2007.
DOI : 10.21236/ada459827

URL : http://www.dtic.mil/dtic/tr/fulltext/u2/a459827.pdf

H. Hersbach, Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems, Weather and Forecasting, vol.15, issue.5, pp.559-570, 2000.

, His research interests include statistical modeling, forecasting techniques, time series analysis, spatio-temporal regression models, and their applications to solar and wind generation modeling, Xwégnon Ghislain Agoua received a Master's degree in Statistics, 2014.