Electricity Price Forecasting Using Generalized Regression Neural Network (GRNN) for PJM Electricity Market

M. M. Tripathi(1*), K. G. Upadhyay(2), S. N. Singh(3)

(1) DOEACC Society Gorakhpur centre, M. M. M. Engineering College campus, India
(2) Electrical Engg. Department, M. M. M. Engineering College campus, India
(3) Electrical Engineering Department, I.I.T. Kanpur, India
(*) Corresponding author

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In competitive electricity markets, price forecasting is becoming increasingly relevant to power producers and consumers. Price forecasts provide crucial information for power producers and consumers to develop bidding strategies in order to maximize benefit. This paper provides a method for predicting day-ahead electricity prices in the PJM market using General Regression Neural Network (GRNN) computing technique. Publicly available data acquired from the PJM electricity market were used for training and testing the ANN. The results obtained through the simulation show that the proposed algorithm is efficient, accurate and produce better results.
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Generalized Regression Neural Network; Radial Basis Function Network; Day-Ahead Electricity Market; Price Forecasting

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S. Vucetic, K. Tomsovic, and Z. Obradovic, Discovering price-load relationships in California’s electricity market, IEEE Trans. on Power Sys., vol. 16, no. 2, May 2001, pp. 280-286.

T. Mount, Market power and price volatility in restructured markets for electricity, Decision supp. Sys., vol. 30, no. 3, 2001, pp. 311-325.

S. Stoft, Power System Economics, Designing Markets for Electricity, New York: IEEE Press, 2002.

H. Y. Yamin, S. M. Shahidehpour, and Z. Li, Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets, Elect. Power and Energy Sys., vol. 26, (2004), pp. 571-581.

F. J. Nogales, J. Contreras, A. J. Conejo, and R. Espinola, Forecasting next-day electricity prices by Time Series Model, IEEE Trans. on Power Sys., vol. 17, no. 2, August 2002, pp. 342-348.

D. W. Bunn, Forecasting loads and prices in competitive power markets, Proceedings of the IEEE, vol. 88, no. 2, Feb. 2000, pp. 163-169.

N. Amjady and M. Hemmati, “Energy price forecasting - problem and proposals for such predictions,” IEEE Power and Energy Magazine, vol. 4, no. 2, March-April 2006, pp. 20-29.

PJM Web Site, http://www.pjm.com, Active March 2006.

Y. Y. Hong, C. Y. Hsiao, “Locational marginal price forecasting in deregulated electricity markets using artificial intelligence,” IEEE Proceedings- Gen. Trans. Dist. vol. 149, no. 5, Sept. 2002, pp. 621-626.

J. Bastian, Zhu J., Banunarayanan V., and Mukerji R., Forecasting energy prices in a competitive market, IEEE Comp. App. in Power, vol. 12, no. 3, July 1999, pp. 40-45.

J. Contreras, R. Espinola, F. J. Nogales, and A. J. Conejo, ARIMA models to predict next-day electricity prices, IEEE Trans. on Power Sys., vol. 18, no. 3, Aug. 2003, pp. 1014-1020.

P. Mandal, T. Senjyu, and T. Funabashi, Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market, Energy Conv. and Management, Volume 47, Issues 15-16, September 2006, Pages 2128-2142.

B. R. Szkuta, L. A. Sanabria, and T. S. Dillon, “Electricity price short term forecasting using ANN,” IEEE Trans. on Power Sys., vol. 14, no. 3, pp. 851-857, Aug. 1999.

C. P. Rodriguez and G. J. Anders, Energy price forecasting in the Ontario competitive power system market, IEEE Trans. on Power Sys., vol. 19, no. 3, Feb. 2004, pp. 366-374.

A.J. Conejo, J. Contrearas, R. Espinola, and M.A. Plazas, Forecasting Electricity Prices for a day ahead pool based electric energy market, Int. Jour. of Forecasting, vol. 21, 2005, pp. 435–462.

A. M. Gonzalez, A. M. S. Roque, J. Gargia-Gonzalez, Modeling and forecasting electricity prices with input/output hidden Markov models, IEEE Trans. on Power Sys. vol. 20, no. 1, 2005, pp 13-24.

R. Gareta , L. M. Romeo, A. Gil, Forecasting of electricity prices with neural networks, Energy Conv. and Management vol. 47, 2003, pp. 1770-1778.

H. S. Hippert, C. E. Pedreira, and R. C. Souza, “Neural networks for short-term load forecasting: A review and evaluation,” IEEE Trans. on Power Sys., vol. 16, no. 1, 2001, pp. 44-55.

T. Senjyu, P. Mandal, K. Uezato, and T. Funabashi, “Next day load curve forecasting using hybrid correction method,” IEEE Trans. on Power Sys., vol. 20, no. 1, 2005, pp. 102-109.

T. Senjyu, P. Mandal, K. Uezato, and T. Funabashi, “Next day load curve forecasting using recurrent neural network structure,” IEEE Proceedings- Gen. Trans. Dist., vol. 151, no. 3, May 2004, pp. 388-394.

E. NI and P. B. Luh, “Forecasting power market clearing price and its discrete PDF using a bayesian-based classification method,” IEEE PES Winter Meeting, vol. 28, Colombus, OH, USA, 2001.

F. Gao, X. Guan, X. R. Cao, and A. Papalexopoulos, Forecasting power market clearing price and quantity using a neural network, IEEE PES Summer Meeting, Seattle, WA, 2000.

D.K. Ranaweera, N.F. Hubele and A.D. Papalexopoulos, “Application of Radial Basis Function Neural Network Model for Short Term Load Forecasting”, IEEE Proceedings- Gen. Trans. Dist., 142 (1995) 45-50.

Paras Mandal, Tomonobu Senjyu, Naomitsu Urasaki, Toshihisa Funabashi, and Anurag K. Srivastava, Electricity Price Forecasting for PJM Day-Ahead Market, IEEE PSCE 19(2006)142.

Guang Li, Chen-Ching Liu, Chris Mattson, and Jacques Lawarrée, Day-Ahead Electricity Price Forecasting in a Grid Environment, IEEE Trans. on Power Sys., VOL. 22, NO. 1, February 2007, pp 266-274.

Paras Mandal, Tomonobu Senjyu, Naomitsu Urasaki, Atsushi Yona, Toshihisa Funabashi and Anurag K. Srivastava, Price Forecasting for Day-Ahead Electricity Market Using Recursive Neural Network, IEEE PES General Meeting, 2007, pp1 – 8.

Alicia Troncoso Lora, Jesús M. Riquelme Santos, Antonio Gómez Expósito, José Luis Martínez Ramos and José C. Riquelme Santos, Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques, IEEE Trans. on Power Sys., Vol. 22, No. 3, August2007, 1294.

J.P.S. Catal˜ao a, S.J.P.S. Mariano a, V.M.F. Mendesb, L.A.F.M. Ferreira c, Short-term electricity prices forecasting in a competitive market: A neural network approach, EPSR 77 (2007) 1297–1304.

Paras Mandal a, Tomonobu Senjyu a,*, Toshihisa Funabashi b, Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market, Energy Con. and Management 47 (2006) 2128–2142.

Hsiao-Tien Pao, Forecasting electricity market pricing using artificial neural networks Energy Con. and Management 48 (2007) 907–912.

Diego J. Pedregal, Juan R. Trapero, Electricity prices forecasting by automatic dynamic harmonic regression models Energy Con. and Management 48 (2007) 1710–1719.


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