The effects of pre-processing methods on forecasting improvement of Artificial Neural Networks

Australian Journal of Basic and Applied Sciences

Volumn 5, Issue 6, 2011, Pages 570-580

  Azadeh, A ^a   Sheikhalishahi, M ^a   Tabesh, M ^a   Negahban, A ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

Author keywords

Artificial Neural Network; Forecasting; Improvement; Pre Processing

Indexed keywords

EID: 83355176798     PISSN: 19918178     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. Azadeh, A., S.F. Ghaderi and S. Sohrabkhani, 2007. Forecasting electrical consumption by integration of Neural Network, time series and ANOVA', Applied Mathematics and Computation, 186: 1753-1761.
2. Azadeh, A., M. Saberi, S.F. Ghaderi, A. Gitiforouz and V. Ebrahimipour, 2008. 'Improved estimation of electricity demand function by integration of fuzzy system and data mining approach', Energy Conversion and Management, 49: 2165-2177.
3. Abraham, A. and B.A. Nath, 2001. 'neuro-fuzzy approach for modeling electricity demand in Victoria', Applied Soft Computing, 1(2): 127-138.
4. Cannas, B., A. Fanni, L. See and G. Sias, 2006. 'Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning', Physics and Chemistry of the Earth, 31: 1164-1171.
5. Chatfield, C., 1992. The Analysis of Time Series, CHAPMAN & HALL.
6. Cheng, C.H. and L.Y. Wei, 2009. 'One step-ahead ANFIS time series model for forecasting electricity loads', optimization and engineering, 0(0): 1-15.
7. Goia, A., C. May and G. Fusai, 2010. 'Functional clustering and linear regression for peak load forecasting', International Journal of Forecasting, in press.
8. Ghiassi, M., D.K. Zimbra and H. Saidane, 2006. 'Medium term system load forecasting with a dynamic artificial neural network model', Electric Power Systems Research, 76: 302-316.
9. Huang, H., R. Hwang and J. Hsieh, 2002. 'A new artificial intelligent peak power load forecaster based on non-fixed neural networks', Electrical Power & Energy Systems, 24(3): 245-250.
10. Kandil, N., R. Wamkeue, M. Saad and S. Georges, 2006. 'An efficient approach for short term load forecasting using artificial neural networks', Electrical Power and Energy Systems, 28(8): 525-530.
11. Park, J.H., Y.M. Park and K.Y. Lee, 1991. 'Composite modeling for adaptive short-term load forecasting', IEEE Transactions on Power Systems, 6(2): 450-457.
12. Schalkoff, R.J., 1997. Artificial Neural Networks, McGraw Hill.
13. Senjyu, T., P. Mandal, K. Uezato and T. Funabashi, 2004. 'Next day load curve forecasting using recurrent neural network structure', IEE Proceedings Generation, Transmission and Distribution, 151(3): 388-394.
14. Taylor, J.W. and R. Buizza, 2003. 'using weather ensemble predictions in electricity demand forecasting', International Journal of Forecasting, 19(1): 57-70.
15. Villalba, S.A. and C.A. Bel, 2000. 'Hybrid demand model for load estimation and short-term load forecasting in distribution electric systems'. IEEE Trans Power Deliver, 15(2): 764-769.
16. Vahidinasab, V., S. Jadid and A. Kazemi, 2008. 'Day-ahead price forecasting in restructured power systems using artificial neural networks', Electric Power Systems Research, Volume, 78(8): 1332-1342.
17. Xiao, Z., S.J. Ye, B. Zhong and C.X. Sun, 2009. 'BP neural network with rough set for short term load forecasting', Expert Systems with Applications, 36(1): 273-279.
18. Yang, J. and J. Stenzel, 2006. 'Short-term load forecasting with increment regression tree', Electric Power Systems Research, 76(9-10): 880-888.
19. Zhang, G.P. and M. Qi, 2005. 'Neural network forecasting for seasonal and trend time series', European Journal of Operational Research, 160: 501-514.