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Classification of Day-Ahead Deregulated Electricity Market Prices Using DCT-CFNN

  • S. Anbazhagan
  • Narayanan Kumarappan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)

Abstract

Artificial neural networks (ANNs) are promising methods for the pattern recognition and classification. In this paper applies ANN to day-ahead deregulated energy market prices. The optimal profit is determined by applying a perfect price forecast. A price forecast with a less prediction errors, yields maximum profits for market players. The numerical electricity price forecasting is high in forecasting errors of various approaches. In this paper, discrete cosine transforms (DCT) based cascade-forward neural network (CFNN) approach (DCT-CFNN) is used to classify the electricity markets of mainland Spain and New York is presented. These electricity price classifications are important because all market participants do not to know the exact value of future prices in their decision-making process. In this paper, classifications of electricity market prices with respect to pre-specified electricity price threshold are used. In this proposed approach, all time series (historical price series) are transformed from time domain to frequency domain using DCT. These discriminative spectral co-efficient forms the set of input features and are classified using CFNN. It has been observed that features selected from spectral domain improve the classification accuracy. The proposed model is more effective compared to some of the most recent price classification models.

Keywords

Price forecasting Discrete cosine transforms Cascade-forward neural network Electricity price classification Electricity market 

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References

  1. 1.
    Singh, S.N.: Electric Power Generation, Transmission and Distribution. Prentice-Hall, India (2008)Google Scholar
  2. 2.
    Conejo, A.J., Plazas, M.A., Espinola, R., Molina, A.B.: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE T. Power Syst. 20, 1035–1042 (2005)CrossRefGoogle Scholar
  3. 3.
    Zareipour, H., Janjani, A., Leung, H., Motamedi, A., Schellenberg, A.: Classification of Future Electricity Market Prices. IEEE T. Power Syst. 26, 165–173 (2011)CrossRefGoogle Scholar
  4. 4.
    Qahwaji, R., Colak, T.: Neural Network-based Prediction of Solar Activities. In: Proceedings of 3rd International Conference on Cybernetics and Information Technologies, Orlando, Florida, USA (2006)Google Scholar
  5. 5.
    Kim, J., Mowat, A., Poole, P., Kasabov, N.: Linear and non-linear pattern recognition models for classification of fruit from visiblenext term–near infrared spectra. Chemometr. Intell. Lab. 51, 201–216 (2000)CrossRefGoogle Scholar
  6. 6.
    Amjady, N., Hemmati, H.: Day-ahead price forecasting of electricity markets by a hybrid intelligent system. Eur. Trans. Elect. Power 19, 89–102 (2009)CrossRefGoogle Scholar
  7. 7.
    Aggarwal, S.K., Saini, L.M., Kumar, A.: Day-ahead price forecasting in Ontario electricity market using variable-segmented support vector machine-based model. Elect. Power Compon. Syst. 37, 495–516 (2009)CrossRefGoogle Scholar
  8. 8.
    Saini, L.M., Aggarwal, S.K., Kumar, A.: Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in national electricity market. IET Gen. Transm. Distrib. 4, 36–49 (2010)CrossRefGoogle Scholar
  9. 9.
    Zhang, L., Luh, P.B.: Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method. IEEE T. Power Syst. 20, 59–66 (2005)CrossRefGoogle Scholar
  10. 10.
    Aggarwal, S.K., Saini, L.M., Kumar, A.: Electricity price forecasting in deregulated markets: A review and evaluation. Int. J. Elec. Power 3, 13–22 (2009)CrossRefGoogle Scholar
  11. 11.
    Unsihuay-Vila, C., Zambroni de Souza, A.C., Marangon-Lima, J.W., Balestrassi, P.P.: Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model. Int. J. Elect. Power 32, 108–116 (2010)CrossRefGoogle Scholar
  12. 12.
    Shafie-khah, M., Moghaddam, M.P., Sheikh-El-Eslami, M.K.: Price forecasting of day-ahead electricity markets using a hybrid forecast method. Energ. Convers. Manage. 52, 2165–2169 (2011)CrossRefGoogle Scholar
  13. 13.
    Anbazhagan, S., Kumarappan, N.: A neural network approach to day-ahead deregulated electricity market prices classification. Electr. Pow. Syst. Res. 86, 140–150 (2012)CrossRefGoogle Scholar
  14. 14.
    Anbazhagan, S., Kumarappan, N.: Day-ahead deregulated electricity market price classification using neural network input featured by DCT. Int. J. Elec. Power 37, 103–109 (2012)CrossRefGoogle Scholar
  15. 15.
    Catalão, J.P.S., Mariano, S.J.P.S., Mendes, V.M.F.: Short-term electricity prices forecasting in a competitive market: A neural network approach. Electr. Pow. Syst. Res. 77, 1297–1304 (2007)CrossRefGoogle Scholar
  16. 16.
    Amjady, N., Keynia, F.: Application of a new hybrid neuro-evolutionary system for day-ahead price forecasting of electricity markets. Appl. Soft Comput. 10, 784–792 (2010)CrossRefGoogle Scholar
  17. 17.
    Spanish Electricity Market Website, http://www.omel.com
  18. 18.
    New York Electricity Market Website, http://www.nyiso.com

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • S. Anbazhagan
    • 1
  • Narayanan Kumarappan
    • 1
  1. 1.Department of Electrical EngineeringAnnamalai UniversityCuddalore DistrictIndia

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