[1] SS. Soman, H. Zareipour, O. Malik, P. Mandal, ‘A review of wind power and wind speed forecasting methods with different time horizons’, In Proceedings of the 2010 north American power symposium (NAPS), p: 1–8, 2010.
[2] J. Zack, ‘Overview of wind energy generation forecasting’, Albany, NY:TrueWind Solutions, LLC. & AWS Scientific, Inc,2003.
[3] L. Soder, ‘Simulation of wind speed forecast errors for operation planning of multi-area power systems’, 8th International conference on probabilistic methods applied to power systems, Iowa state university, p: 723-28, 2004.
[4] X. Wang, G. Sideratos, N. Hatziargyriou, LH. Tsoukalas, ‘Wind speed forecasting for power system operational planning’, 8th International conference on probabilistic methods applied to power systems, Iowa state university, p: 470-74, 2004.
[5] M. Monfared, H. Rastegar, H. Madadi Kojabadi, ‘A new strategy for wind speed forecasting using artificial intelligent methods’, Renewable Energy 34, p: 845-848, 2009.
]6[ ی. نوراللهی، م. جوکار، م. ساتکین، "استفاده از الگوریتم روزهای مشابه جهت بالا بردن دقت تخمین پیش بینی کوتاه مدت سرعت باد به کمک شبکه های عصبی"، دومین کنفرانس انرژی بادی ایران ، 1393.
[7] B. Zhu, M. Chen, N. Wade, L. Ran, ‘A prediction model for wind farm power generation based on fuzzy modeling’, Procedia Environmental Sciences 12, p:122-129, 2012.
[8] Y. Freund, , and R. Schapire, ‘Experiments with a new boosting algorithm’, in Proceeding of the Thirteenth International Conference on Machine Learning, pp. 148-156, 1996.
[9] K. Chen, L. Xu, H. Chi, Improved learning algorithms for mixture of experts in multiclass classification. Neural Network 12(9):1229–1252, 1999.
[10] R. Ebrahimpour, and N. Sadeghnejad, ,’ Boost-wise pre-loaded mixture of experts for classification tasks’ Neural Comput & Applic, DOI 10.1007/s00521-012-0909-2, 2012.
[11] R. Avnimelech, and N. Intrator, ‘Boosted mixture of experts: An ensemble learning scheme’, Neural Computation, vol. 11, no. 2, pp.483-497, 1999.
[12] M.Jordan, and R. Jacobs, ‘Modular and Hierarchical Learning Systems’. The Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, MA, 1995.
[13] R. Ebrahimpour, A. Esmkhani , S. Faridi , Farsi handwritten digit recognition based on mixture of RBF experts. IEICE Electron Exp 7(14):1014–1019, 2011.
[14] R. Ebrahimpour, H. Nikoo, S. Masoudnia, M. Yousefi, M. Ghaemi, Mixture of MLP experts for trend forecasting of time-series: a case study of Tehran Stock Exchange. Int J Forecast 27(3):804–816, 2011.
[15] Waterhouse S, Cook G, Ensemble methods for phoneme classification. In: Mozer M, Jordan J, Petsche T (eds) Advances in neural information processing systems. MIT Press, Cambridge, 1997.
[16] http://www.suna.org.ir