Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs

Wind energy is currently one of the types of renewable energy with a large generation capacity. However, since the operation of wind power generation is challenging due to its intermittent characteristics, forecasting wind power generation efficiently is essential for economic operation. This paper proposes a new method of wind power and speed forecasting using a multi-layer feed-forward neural network (MFNN) to develop forecasting in time-scales that can vary from a few minutes to an hour. Inputs for the MFNN are modeled by fuzzy numbers because the measurement facilities provide maximum, average and minimum values. Then simultaneous perturbation stochastic approximation (SPSA) algorithm is employed to train the MFNN. Real wind power generation and wind speed data measured at a wind farm are used for simulation. Comparative studies between the proposed method and traditional methods are shown.

[1]  J. Spall.  Multivariate stochastic approximation using a simultaneous perturbation gradient approximation , 1992 .

[2]  László Gerencsér,et al.  The use of the SPSA method in ECG analysis , 2002, IEEE Transactions on Biomedical Engineering.

[3]  E. El-Saadany,et al.  Grey predictor for wind energy conversion systems output power prediction , 2006, IEEE Transactions on Power Systems.

[4]  Chin-Teng Lin,et al.  Neural fuzzy systems , 1994 .

[5]  J. Spall.  Implementation of the simultaneous perturbation algorithm for stochastic optimization , 1998 .

[6]  N.D. Hatziargyriou,et al.  An Advanced Statistical Method for Wind Power Forecasting , 2007, IEEE Transactions on Power Systems.

[7]  Kasım Koçak,et al.  Examination of persistence properties of wind speed records using detrended fluctuation analysis , 2009 .

[8]  F. Rezayat.  Constrained SPSA controller for operations processes , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[9]  G. A. Coutie,et al.  Short-term forecasting , 1965 .

[10]  I. Erlich,et al.  European Balancing Act , 2007, IEEE Power and Energy Magazine.

[11]  J. Spall,et al.  Nonlinear adaptive control using neural networks: estimation with a smoothed form of simultaneous perturbation gradient approximation , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[12]  W.L. Kling,et al.  Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch , 2007, IEEE Transactions on Energy Conversion.

[13]  Babajide O. Familoni,et al.  A diagonal recurrent neural network-based hybrid direct adaptive SPSA control system , 1999, IEEE Trans. Autom. Control..

[14]  A. Shamshad,et al.  First and second order Markov chain models for synthetic generation of wind speed time series , 2005 .

[15]  J.A.P. Lopes,et al.  On the optimization of the daily operation of a wind-hydro power plant , 2004, IEEE Transactions on Power Systems.

[16]  J.B. Theocharis,et al.  Long-term wind speed and power forecasting using local recurrent neural network models , 2006, IEEE Transactions on Energy Conversion.

[17]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

[18]  Michael C. Fu,et al.  Optimal structured feedback policies for ABR flow control using two-timescale SPSA , 2001, TNET.

[19]  J. Spall,et al.  Model-free control of nonlinear stochastic systems with discrete-time measurements , 1998, IEEE Trans. Autom. Control..

[20]  P. Pinson,et al.  Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power , 2007, IEEE Transactions on Power Systems.

[21]  Henrik Madsen,et al.  Skill forecasting from ensemble predictions of wind power , 2009 .

[22]  J. Spall,et al.  Optimal random perturbations for stochastic approximation using a simultaneous perturbation gradient approximation , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[23]  J. L. Maryak,et al.  Simultaneous perturbation optimization for efficient image restoration , 2005, IEEE Transactions on Aerospace and Electronic Systems.