WIND FARM POWER PREDICTION AND UNCERTAINTY QUANTIFICATION

In the recent years, due to the increase in the average temperature and environmental pollution and also the demand for energy, finding new resources for energy generation has been a big challenge for the governments. Among the various renewable energy resources, the energy derived from the wind farms has absorbed a great deal of attention. Due to the increase in the power generated by the wind power plants and their influence in the power systems and according to their variable and oscillatory structure, accurate prediction of the wind farms output power is required for their participation in the electric market. This prediction faces to some uncertainties. The wind farms owners should have a distinct strategy and sale program for trading in the electric market. This program should reflex the uncertainties. In this paper, Wavelet-Radial basis function (WT+RBF) is used for the prediction of the output power of the Tetrapolis Kefalonia in Greece and the uncertainties values is calculated using Quantile regression method.

[1]  Detlev Heinemann,et al.  Relating the uncertainty of short-term wind speed predictions to meteorological situations with methods from synoptic climatology , 2004 .

[2]  Yuan-Kang Wu,et al.  A literature review of wind forecasting technology in the world , 2007, 2007 IEEE Lausanne Power Tech.

[3]  Chun-Lien Su.  Probabilistic load-flow computation using point estimate method , 2005, IEEE Transactions on Power Systems.

[4]  Ignacio J. Ramirez-Rosado,et al.  Comparison of two new short-term wind-power forecasting systems , 2009 .

[5]  Ángel M. Pérez-Bellido,et al.  Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction , 2009 .

[6]  N. D. Hatziargyriou,et al.  Probabilistic Wind Power Forecasting Using Radial Basis Function Neural Networks , 2012, IEEE Transactions on Power Systems.

[7]  P. McSharry,et al.  Short-Term Load Forecasting Methods: An Evaluation Based on European Data , 2007, IEEE Transactions on Power Systems.

[8]  G. B. Gharehpetian,et al.  Probabilistic DC load-flow based on Two-Point Estimation (T-PE) method , 2010, 2010 4th International Power Engineering and Optimization Conference (PEOCO).

[9]  S. N. Singh,et al.  AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network , 2012, IEEE Transactions on Sustainable Energy.

[10]  Tomonobu Senjyu,et al.  A Novel Hybrid Approach Based on Wavelet Transform and Fuzzy ARTMAP Networks for Predicting Wind Farm Power Production , 2013 .

[11]  George Galanis,et al.  Wind power prediction based on numerical and statistical models , 2013 .

[12]  R. Kavasseri,et al.  Day-ahead wind speed forecasting using f-ARIMA models , 2009 .

[13]  P. Pinson,et al.  Uncertainty of short-term wind power forecasts a methodology for on-line assessment , 2004, 2004 International Conference on Probabilistic Methods Applied to Power Systems.

[14]  Wang Chengshan.  Short-Term Wind Power Prediction Method , 2013 .

[15]  Yongqian Liu,et al.  Uncertainty Analysis of Wind Power Prediction Based on Quantile Regression , 2012, 2012 Asia-Pacific Power and Energy Engineering Conference.

[16]  Paras Mandal,et al.  A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

[17]  M. M. Ardehali,et al.  Very short-term wind speed prediction: A new artificial neural network–Markov chain model , 2011 .