A. Mellit, S. Kalogirou, L. Hontoria, and S. Shaari, Artificial intelligence techniques for photovoltaic applications: A review, Progress in Energy and Combustion Science, vol.34, issue.5, pp.52-76, 2008.
DOI : 10.1016/j.pecs.2008.01.001

J. Mubiru, Predicting total solar irradiation values using artificial neural networks, Renewable Energy, vol.33, issue.10, pp.2329-2332, 2008.
DOI : 10.1016/j.renene.2008.01.009

J. Mubiru and E. Banda, Estimation of monthly average daily global solar irradiation using artificial neural networks, Solar Energy, vol.82, issue.2, pp.181-187, 2008.
DOI : 10.1016/j.solener.2007.06.003

S. Kalogirou, Artificial neural networks in renewable energy systems applications: a review, Renewable and Sustainable Energy Reviews, vol.5, issue.4, pp.373-401, 2001.
DOI : 10.1016/S1364-0321(01)00006-5

F. Hocaoglu, O. Gerek, and M. Kurban, Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks, Solar Energy, vol.82, issue.8, pp.714-726, 2008.
DOI : 10.1016/j.solener.2008.02.003

L. Zarzalejo, L. Ramirez, and J. Polo, Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index. Energy, pp.1685-1697, 2005.

K. Jain, M. Jianchang, and K. Mohiuddin, Artificial neural networks: a tutorial, Computer, vol.29, issue.3, pp.31-44, 1996.
DOI : 10.1109/2.485891

S. Crone, Stepwise Selection of Artificial Neural Networks Models for Time Series Prediction Journal of Intelligent Systems, 2005.

Y. Jiang, Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models. Energy, pp.1276-1283, 2009.

M. Benghanem and A. Mellit, Radial Basis Function Network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia, Energy, vol.35, issue.9, pp.3751-3762, 2010.
DOI : 10.1016/j.energy.2010.05.024

J. Faraway and C. Chatfield, Times series forecasting with neural networks: a case study, Research report 95-06 of the statistics group, 1995.

J. Hamilton, Times series analysis, 1994.

R. Bourbonnais and M. Terraza, Analyse des séries temporelles, 2008.

G. Celeux and J. Nakache, Analyse discriminante sur variables qualitatives

M. Muselli, P. Poggi, G. Notton, and A. Louche, First Order Markov Chain Model for Generating Synthetic 'Typical Days' Series of Global Irradiation in Order to Design PV Stand Alone Systems, Energy Conversion and Management, pp.42-6675, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00541261

D. Logofet and E. Lesnaya, The mathematics of Markov models: what Markov chains can really predict in forest successions, Ecological Modelling, vol.126, issue.2-3, pp.285-298, 2000.
DOI : 10.1016/S0304-3800(00)00269-6

M. Sharif and D. Burn, Simulating climate change scenarios using an improved K-nearest neighbor model, Journal of Hydrology, vol.325, issue.1-4, pp.1-4179, 2006.
DOI : 10.1016/j.jhydrol.2005.10.015

S. Yakowitz, NEAREST-NEIGHBOUR METHODS FOR TIME SERIES ANALYSIS, Journal of Time Series Analysis, vol.21, issue.2, pp.235-247, 1987.
DOI : 10.2307/2288075

A. Mellit, S. Kalogirou, L. Hontoria, and S. Shaari, Artificial intelligence techniques for sizing photovoltaic systems: A review, Renewable and Sustainable Energy Reviews, vol.13, issue.2, pp.406-419, 2009.
DOI : 10.1016/j.rser.2008.01.006

C. Paoli, C. Voyant, M. Muselli, and M. Nivet, Solar radiation forecasting using ad-hoc time series preprocessing and neural networks, " Emerging Intelligent Computing technology and Applications, pp.898-907, 2009.

C. Voyant, M. Muselli, C. Paoli, M. Nivet, P. Poggi et al., Predictability of PV power grid performance on insular sites without weather stations: use of artificial neural networks, pp.10-4229
URL : https://hal.archives-ouvertes.fr/hal-00442312

B. Liu and R. Jordan, Daily sunshine duration on surfaces tilted towards the equator, Trans SHRAE, vol.67, pp.526-541, 1962.

V. Badescu, Modelling Solar radiation at the earth surface, recent advances, 2008.

D. Reindl, W. Beckman, and J. Duffie, Evaluation of hourly tilted surface radiation models, Solar Energy, vol.45, issue.1, pp.9-17, 1990.
DOI : 10.1016/0038-092X(90)90061-G

R. Perez, P. Ineichen, and R. Seals, Modeling daylight availability and irradiance components from direct and global irradiance, Solar Energy, vol.44, issue.5, pp.44-5271, 1990.
DOI : 10.1016/0038-092X(90)90055-H

URL : http://archive-ouverte.unige.ch/unige:17206

H. Elminir, Y. Azzam, and F. Younes, Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models. Energy, Aout, vol.32, issue.8, pp.1513-1523, 2007.

J. Hay and J. Davies, Calculation of the solar radiation incident on an inclined surface, Proc. First Canadian Solar radiation workshop, pp.59-72, 1980.

P. Ineichen, O. Guisan, and R. Perez, Ground-reflected radiation and albedo, Solar Energy, vol.44, issue.4, pp.44-4207, 1990.
DOI : 10.1016/0038-092X(90)90149-7

G. Zhang and M. Qi, Neural network forecasting for seasonal and trend time series, European Journal of Operational Research, vol.160, issue.2, pp.501-514, 2005.
DOI : 10.1016/j.ejor.2003.08.037

B. Richard, E. Hulstrom, and L. , A Simplified Clear Sky Model for Direct and Diffuse Sunshine duration on Horizontal Surfaces, SERI/TR-642-761, 1981.

J. Cao and S. Cao, Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis. Energy, pp.3435-3445, 2006.

P. Ineichen, A broadband simplified version of the Solis clear sky model, Solar Energy, vol.82, issue.8, pp.758-762, 2008.
DOI : 10.1016/j.solener.2008.02.009

R. Mueller, K. Dagestad, P. Ineichen, M. Schroedter-homscheidt, S. Cros et al., Rethinking satellite-based solar irradiance modellingThe SOLIS clear-sky module, Remote Sensing of Environment, vol.91, issue.2, pp.160-174, 2004.
DOI : 10.1016/j.rse.2004.02.009

C. Paoli, C. Voyant, M. Muselli, and M. Nivet, Forecasting of preprocessed daily solar radiation time series using neural network. Solar Energy

M. Costa, A. Braga, and B. Menezes, Improving generalization of MLPs with sliding mode control and the Levenberg???Marquardt algorithm, Neurocomputing, vol.70, issue.7-9, pp.7-91342, 2007.
DOI : 10.1016/j.neucom.2006.09.003

A. Sfetsos and A. Coonick, Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques, Solar Energy, vol.68, issue.2, pp.169-178, 2000.
DOI : 10.1016/S0038-092X(99)00064-X

N. Muttil and K. Chau, Machine-learning paradigms for selecting ecologically significant input variables, Engineering Applications of Artificial Intelligence, vol.20, issue.6, pp.735-744, 2007.
DOI : 10.1016/j.engappai.2006.11.016

T. Box, G. P. Jenkins, and G. , Time Series Analysis: Forecasting and Control, 1976.
DOI : 10.1002/9781118619193

D. Ahlburg, Error measures and the choice of a forecast method, International Journal of Forecasting, vol.8, issue.1, pp.99-100, 1992.
DOI : 10.1016/0169-2070(92)90010-7

M. Toukourou, A. Johannet, and G. Dreyfus, Flash Flood Forecasting by Statistical Learning in the Absence of Rainfall Forecast: A Case Study, Engineering Applications of Neural Networks, vol.16, issue.6, pp.98-107, 2009.
DOI : 10.1029/WR016i006p01034

G. Notton, P. Poggi, and C. Cristofari, Predicting hourly solar irradiations on inclined surfaces based on the horizontal measurements: Performances of the association of well-known mathematical models, Energy Conversion and Management, vol.47, issue.13-14, pp.1816-1829, 2006.
DOI : 10.1016/j.enconman.2005.10.009

URL : https://hal.archives-ouvertes.fr/hal-00541232

A. Noorian, I. Moradi, and G. Kamali, Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces, Renewable Energy, vol.33, issue.6, pp.1406-1412, 2008.
DOI : 10.1016/j.renene.2007.06.027

E. Dubois and E. Michaux, Grocer: an econometric toolbox for Scilab, 2008.