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Recent Advances in Electrical & Electronic Engineering

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ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

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Research Article

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

Author(s): Sumit Saroha* and Sanjeev K. Aggarwal

Volume 13, Issue 1, 2020

Page: [16 - 26] Pages: 11

DOI: 10.2174/2352096512666190118160604

Price: $65

Abstract

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy.

Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique.

Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric.

Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

Keywords: General regression neural network, time series, wavelet transform, wind power forecasting, market data, naïve predictor.

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[1]
J. Yan, H. Zhang, Y. Liu, S. Han, L. Li, and Z. Lu, "Forecasting the high penetration of wind power on multiple scales using multi-to-multi mapping", IEEE Trans. Power Syst., vol. 33, no. 3, pp. 3276-3284, 2018.
[2]
X. Wang, C. Wang, and Q. Li, "Short-term wind power prediction using GA-ELM", The Open Electr. Electron. Eng. J., vol. 11, pp. 48-56, 2017.
[3]
N.S. Pearre, and L.G. Swan, "Statistical approach for improved wind speed forecasting for wind power production", Sustain. Energ. Technol. Assess., vol. 27, pp. 180-191, 2018.
[4]
Global Wind Energy Council Report. Available at: www.gwec.net (Accessed May 30, 2018).
[5]
R.G. Kavasseri, and K. Seetharaman, Day-ahead wind speed forecasting using f-ARIMA models.Renew. Energ, . Vol. 34, pp. 1388-1393, 2009.
[6]
D.C. Hill, D. McMillan, K.R.W. Bell, and D. Infield, "Application of auto-regressive models to U.K. wind speed data for power system impact studies", IEEE Transact. Sustain. Energ., vol. 3, no. 1, pp. 134-141, 2012.
[7]
E. Erdem, and J. Shi, "ARMA based approaches for forecasting the tuple of wind speed and direction", Appl. Energy, vol. 88, pp. 1405-1414, 2011.
[8]
H. Lei, S. Jie, C. Qiong, and J.G. Xiu, "Wind power forecasting base on ARMAX-GARCH for a microgrid", Adv. New Renew. Energ., vol. 1, no. 3, pp. 224-229, 2013.
[9]
E. Cadenas, and W. Rivera, "Wind speed forecasting in the South Coast of Oaxaca, Mexico", Renew. Energy, vol. 32, pp. 2116-2128, 2007.
[10]
M. Monfared, H. Rastegar, and H.M. Kojabadi, "A new strategy for wind speed forecasting using artificial intelligent methods", Renew. Energy, vol. 34, pp. 845-848, 2009.
[11]
E. Erdem, and W. Rivera, "Short term wind speed forecasting in La Venta, Oaxaca, Mexico, using artificial neural networks", Renew. Energy, vol. 34, pp. 274-278, 2009.
[12]
E. Erdem, and W. Rivera, "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-FFNN model", Renew. Energy, vol. 35, pp. 2732-2738, 2010.
[13]
G. Li, and J. Shi, "On comparing three artificial neural networks for wind speed forecasting", Appl. Energy, vol. 87, pp. 2313-2320, 2010.
[14]
K. Sreelakshmi, and P.R. Kumar, "Short-term wind speed prediction using support vector machine model", WSEAS Trans. Comput., vol. 7, no. 11, pp. 1828-1837, 2008.
[15]
J.P.S. Catalão, H.M.I. Pousinho, and V.M.F. Mendes, "Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal", IEEE Transact. Sustain. Energ., vol. 2, no. 1, pp. 50-59, 2011.
[16]
H. Liu, H.Q. Tian, C. Chen, and Y.F. Li, "A hybrid statistical method to predict wind speed and wind power", Renew. Energy, vol. 35, pp. 1857-1861, 2010.
[17]
X. An, D. Jiang, C. Liu, and M. Zhao, "Wind farm power prediction based on wavelet decomposition and chaotic time series", Expert Syst. Appl., vol. 38, pp. 11280-11285, 2011.
[18]
J.P.S. Catalão, H.M.I. Pousinhom, and V.M.F. Mendes, "Short-term wind power forecasting in Portugal by neural networks and wavelet transform", Renew. Energy, vol. 36, pp. 1245-1251, 2011.
[19]
D. Liu, D. Niu, H. Wang, and L. Fan, "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm", Renew. Energy, vol. 62, pp. 592-597, 2014.
[20]
X. An, D. Jiang, M. Zhao, and C. Liu, "Short-term prediction of wind power using EMD and chaotic theory", Commun. Nonlinear Sci. Numer. Simul., vol. 17, pp. 1036-1042, 2012.
[21]
Ontario Electricity Market Wind Data. http://www.ieso.ca/ (Accessed August 30, 2018).
[22]
S.K. Aggarwal, L.M. Saini, and A. Kumar, "Electricity price forecasting in Ontario electricity market using wavelet transform in artificial neural network based model", Int. J. Control. Autom. Syst., vol. 6, no. 5, pp. 639-650, 2008.
[23]
S.K. Aggarwal, L.M. Saini, and A. Kumar, "Day-ahead Price forecasting in Ontario electricity market using variable-segmented support vector machine-based model", Electr. Power Compon. Syst., vol. 37, pp. 495-516, 2009.
[24]
A. Sfetsos, and C. Siriopoulos, "“Time series forecasting of averaged data with efficient use of information”, IEEE Transact. Syst. Man Cybernet.-Part A", Syst. Humans, vol. 35, no. 5, pp. 738-745, 2005.
[25]
J. Varmaak, and E.C. Botha, "Recurrent neural networks for short-term load forecasting", IEEE Trans. Power Syst., vol. 13, no. 1, pp. 12-132, 1998.
[26]
S. Anbazhagan, and N. Kumarappan, "Day-Ahead deregulated electricity market price forecasting using recurrent neural network", IEEE Syst. J., vol. 7, no. 4, pp. 866-872, 2013.
[27]
D.F. Specht, "A generalized regression neural network", IEEE Transactions on Neural Networks, vol. 2, pp. 568-576, 1991.
[28]
O. Kisi, "A combined generalized regression neural network wavelet model for monthly stream flow prediction", KSCE J. Civ. Eng., vol. 15, no. 8, pp. 1469-1479, 2011.
[29]
H.B. Celikoglu, and H.K. Cigizoglu, "Public transportation trip flow modeling with generalized regression neural networks", Adv. Eng. Software, vol. 38, pp. 71-79, 2007.
[30]
K.N. Filho, D.P. Lotufo, and C.R. Minussi, "Short-term multinodal load forecasting using a modified general regression neural network", IEEE Trans. Power Deliv., vol. 26, no. 4, pp. 2862-2869, 2011.
[31]
Y. Li, and J. Wang, "The load forecasting model based on bayes-GRNN", J. Softw., vol. 7, no. 6, pp. 1273-1280, 2012.
[32]
G. Gross, and F.D. Galiana, "Short-term load forecasting", Proc. IEEE, vol. 75, no. 12, pp. 1558-1573, 1987.

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