REFERENCES
- Alp, U. 2009. A hybrid model for forecasting sales in Turkish paint industry. Int. J. Comput. Int. Sys. 2:277–287.
- Amjady, N. 2006. Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE T, Power Syst. 21:887–896.
- Amjady, N., and Daraeepour, A. 2009. Mixed price and load forecasting of electricity markets by a new iterative prediction method. Electr. Pow. Syst. Res. 79:1329–1336.
- Bouchachia, A. 2009. Radial basis function nets for time series prediction. Int. J. Comput. Int. Sys. 2:147–157.
- Castin, N., Malerba, L., and Domingos, R. P. 2008. Use of computational intelligence for the prediction of vacancy migration energies in atomistic kinetic monte carlo simulations. Int. J. Comput. Int. Sys. 4:340–352.
- Chen, T. C., and Yu, C. H. 2009. Motion control with deadzone estimation and compensation using GRNN for TWUSM drive system. Expert Syst. Appl. 36:10931–10941.
- Contreras, J., Espinola, R., Nogales, F. J., and Conejo, A. J. 2003. ARIMA models to predict next-day electricity prices. IEEE T. Power Syst. 18:1014–1020.
- Diongue, A. K., Guegan, D., and Vignal, B. 2009. Forecasting electricity spot market prices with a k-factor GIGARCH process. Appl. Energ. 24:505–510.
- Garcia, R. C., Contreras, J., Akkeren, M. V., and Garcia, G. A. 2005. A GARCH forecasting model to predict day-ahead electricity prices. IEEE T. Power Syst. 20:867–874.
- Gonzalez, A. M., San Roque, A. M., and Garcia, G. J. 2005. Modeling and forecasting electricity prices with input/output hidden Markov models. IEEE T. Power Syst. 20:13–24.
- Hong, Y. Y., and Hsiao, C. Y. 2002. Locational marginal price forecasting in deregulated electric markets using a recurrent neural network. IEE Proceedings Generation, Transmission & Distribution 149:621–626.
- Mallat, S. 1999. A Wavelet Tour of Signal Processing. 2nd edn. Waltham, MA:Academic Press.
- Nima, A., and Farshid, K. 2008. Day-ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method. Electrical Power and Energy Systems 30:533–546.
- Nogales, F., Contreras, J., and Conejo, A. 2002. Forecasting next-day electricity prices by time series models. IEEE T. Power Sys. 17:342–348.
- Pindoriya, N. M., Singh, S. N., and Singh, S. K. 2008. An adaptive wavelet neural network based energy price forecasting in electricity markets. IEEE T. Power Sys. 23:1423–1432.
- Szkuta, B., Sanavria, L., and Dillon, T. 1999. Electricity price short term forecasting using artificial neural networks. IEEE T. Power Sys. 14:851–857.
- Vahidinasab, V., Jadid, S., and Kazemi, A. 2008. Day-ahead price forecasting in restructured power systems using artificial neural networks. Electr. Pow. Syst. Res. 78:1332–1342.
- Wang, B., Tai, N. L., and Zhai, H. Q. 2008. A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting. Electr. Pow. Syst. Res. 78:1679–1685.
- Yang, S. X., Li, X., and Li, N. 2006. Optimizing neural network forecast by immune algorithm. J. Cent. South Univ. T. 5:573–576.
- Yao, S. J., Song, Y. H., Zhang, L. Z., and Cheng, X. Y. 2000. Prediction of system marginal price by wavelet transform and neural network. Electr. Mach. Pow. Syst. 28:537–49.
- Zhang, L., Luh, P. B., and Kasiviswanathan, K. 2003. Energy clearing price prediction and confidence interval estimation with cascaded neural networks. IEEE T. Pow. Sys. 18:99–105.