Page 141 - Advances in Renewable Energies and Power Technologies
P. 141

114    CHAPTER 3 Forecasting of Intermittent Solar Energy Resource




                         [85] R. Marquez, H.T.C. Pedro, C.F.M. Coimbra, Hybrid solar forecasting method uses sat-
                             ellite imaging and ground telemetry as inputs to ANNs, Solar Energy 92 (2013)
                             176e188.
                         [86] P. Bacher, H. Madsen, H.A. Nielsen, Online short-term solar power forecasting, Solar
                             Energy 83 (10) (2009) 1772e1783.
                         [87] A. Mellit, S.A. Kalogirou, Artificial intelligence techniques for photovoltaic applica-
                             tions: a review, Progr. Energy Combust. Sci. 34 (2008) 547e632.
                         [88] A. Sfetsos, A.H. Coonick, Univariate and multivariate forecasting of hourly solar radi-
                             ation with artificial intelligence techniques, Solar Energy 68 (2000) 169e178.
                         [89] R. Bourbonnais, M. Terraza, Analyse des se ´ries temporelles: application a ` l’e ´conomie
                             et a ` la gestion, second ed., Dunod Edition, Paris, 2008.
                         [90] P.J. Brockwell, R.A. Davis, Time Series: Theory and Methods, second ed., Springer-
                             Verlag, New York, 1991.
                         [91] C. Voyant, P. Randimbivololona, M.L. Nivet, C. Paoli, M. Muselli, 24-hours Ahead
                             Global Irradiation Forecasting Using Multi-layer Perceptron. Meteorological
                             Applications, Wiley, 2013.
                         [92] C. Voyant, C. Paoli, M. Muselli, M.-L. Nivet, Multi-horizon solar radiation forecasting
                             for Mediterranean locations using time series models, Renew. Sustain. Energy Rev. 28
                             (2013) 44e52.
                         [93] J.D. Hamilton, Time Series Analysis, Princeton University Press, 1994, ISBN 0-691-
                             04289-6.
                         [94] G. Reikard, Predicting solar radiation at high resolutions: a comparison of time series
                             forecasts, Solar Energy 83 (3) (2009) 342e349.
                         [95] A. Hammer, D. Heinemann, C. Hoyer-Klick, E. Lorenz, B. Mayer, M. Schroedter-
                             Homscheidt, Remote Sensing and Atmospheric Physics for an Efficient Use of Renew-
                             able Energies, Status Report 2004e2007, Technical Report, Virtual Institute of Energy
                             Meteorology, 2007.
                         [96] J. Remund, R. Perez, E. Lorenz, Comparison of solar radiation forecast for the USA, in:
                             23rd European Photovoltaic Solar Energy Conference, Valencia (Spain), September
                             1e5, 2008, 2008, pp. 3141e3143.
                         [97] R. Perez, K. Moore, S. Wilcox, D. Renne ´, A. Zelenka, Forecasting solar radiatione
                             preliminary evaluation of an approach based upon the national forecast database, Solar
                             Energy 81 (6) (2007) 809e812.
                         [98] E. Lorenz, J. Remund, S.C. Mu ¨ller, W. Traunmu ¨ller, G. Steinmaurer, D. Pozo,
                             J.A. Ruiz-Arias, V.L. Fanego, L. Ramirez, M.G. Romeo, C. Kurz, L.M. Pomares,
                             C.G. Guerrero, Benchmarking of different approaches to forecast solar irradiance, in:
                             24th European Photovoltaic Solar Energy Conference, Hamburg, Germany, September
                             21e25, 2009, 2009.
                         [99] P. Lauret, M. Diagne, M. David, A neural network post-processing approach to
                             improving NWP solar radiation forecasts, Energy Procedia 57 (2014) 1044e1052.
   136   137   138   139   140   141   142   143   144   145   146