[1]
M. Balzani and A. Reatti, Neural network based model of a PV array for the optimum performance of PV system, in Proc. PhD Research in Microelectronics and ElectronicsConf., vol. 2, 2005, pp.123-126.
DOI: 10.1109/rme.2005.1542952
Google Scholar
[2]
I. Ashraf and A. Chandra, Artificial neural network based models for forecasting electricity generation of grid connected solar PV power plant, Int. Journal of Global Energy Issues, vol. 21, no. 1/2, pp.119-130, (2004).
DOI: 10.1504/ijgei.2004.004704
Google Scholar
[3]
Kaoru Furushima, Yutaka Nawata, Michio Sadatomi. Prediction of Photovoltaic Power Output Considering Weather Conditions. ASME 2006 International Solar Energy Conference (ISEC2006), pp.117-118.
DOI: 10.1115/isec2006-99024
Google Scholar
[4]
Miquel Ramon-Marin, Andreas Sumper et al. Prediction of Photovoltaic generation for distribution network planning. International Conference on Renewable Energies and Power Quality, (2009).
Google Scholar
[5]
S. I. Sulaiman, T. K. A. Rahman, and I. Musirin. Partial Evolutionary ANN for Output Prediction of A Grid-connected Photovoltaic System. International Journal of Computer and Electrical Engineering 2009, 1(1): 41-46.
DOI: 10.7763/ijcee.2009.v1.7
Google Scholar
[6]
Yona A, Senjyu T, FunabaShi T, et al. Application of neural network to one-day—ahead 24 hours generating power forecasting for photovoltaic system[J]. Intelligent Systems Applications to Power Systems，2007.
DOI: 10.1109/isap.2007.4441657
Google Scholar
[7]
Monitoring and Control Under Ministry of Education, North China Electric Power University, Baoding 071003, China); Photovoltaic power generation output forecasting based on support vector machine regression technique[J]; Electric Power; 2008-02.
DOI: 10.29252/jafm.13.01.30063
Google Scholar
[8]
S.I. Sulaiman, I. Musirin, and T. K. A. Rahman. Prediction of Grid-Photovoltaic System Output Using Three variate ANN Models. WSEAS Transactions on Information Science and Applications 2009, 6(8): 1339-1348.
Google Scholar
[9]
Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of Longdon, Series A , 1998, 454: 903-995.
DOI: 10.1098/rspa.1998.0193
Google Scholar
[10]
Rummel hart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning representations by back propagating errors. Nature 323, 533-536.
DOI: 10.1038/323533a0
Google Scholar
[11]
Holland J H. Adaptation in Nature and Artificial Systems．MIT Press，(1992).
Google Scholar
[12]
T. Yalcinoz, H. Altun. A new genetic algorithm with arithmetic crossover to economic and environmental economic dispatch Eng. Intell. Syst. Electr. Eng. Commun., 13 (1) (2005), p.45–52.
DOI: 10.1109/afrcon.2002.1160037
Google Scholar
[13]
T. Takahama and S. Sakai, Constraint optimization by α constraint genetic algorithm(αGA), Syst. Comput. Jpn., vol. 35, no. 5, p.11–22, May (2004).
DOI: 10.1002/scj.10562
Google Scholar
[14]
N. Siebert, Development of methods for regional wind power forecasting, Ph.D. dissertation, Ecole des Mines de Paris, (2008).
Google Scholar
[15]
B. van Ginneken and A. Mendrik. Image denoising with k-nearest neighbor and support vector regression. In The 18th International Conference on Pattern Recognition, ICPR'06, volume 3, pages 603–606, Hong Kong, (2006).
DOI: 10.1109/icpr.2006.685
Google Scholar
[16]
J. B. de Oliveira, Intelligent Systems and Wavelets for Forecast Winds and Wind Energy, M.S. thesis, Dep. Elect. Eng., UFPE University, Recife, Brazil, (2008).
Google Scholar
