Short-Term Load Forecasting Using Soft Computing Techniques
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Abstract
Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand is increased with high growth rate. In this paper, a short-term load forecasting realized by a generalized neuron–wavelet method is proposed. The proposed method consists of wavelet transform and soft computing technique. The wavelet transform splits up load time series into coarse and detail components to be the features for soft computing techniques using Generalized Neurons Network (GNN). The soft computing techniques forecast each component separately. The modified GNN performs better than the traditional GNN. At the end all forecasted components is summed up to produce final forecasting load.
- S. E. Papadakis, J. B. Theocharis, S. J. Kiartzis, and A. G. Bakirtzis, “A novel approach to short-term load fore- casting using fuzzy neuralnetworks,” IEEE Transactions on Power Systems, Vol. 13, pp. 480–492, 1998.
- D. K. Chaturvedi, P. S. Satsangi, and P. K. Kalra, “Fuzz- ified neural network approach for load forecasting problems,” International Journal on Engineering Intelli- gent Systems, CRL Publishing, U. K., Vol. 9, No. 1, pp. 3–9, March 2001.
- A. A. Eidesouky and M. M. Eikateb, “Hybrid adaptive techniques for electric-load forecast using ANN and ARI- MA,” IEE Proceedings–Generation, Transmission and Distribution, Vol. 147, pp. 213–217, 2000.
- A. G. Bakirtzis, J. B. Theocharis, S. J. Kiartzis, and K. J. Satsios, “Short term load forecasting using fuzzy neural networks,” IEEE Transactions on Power Systems, Vol. 10, pp. 1518–1524, 1995.
- S. Rahman and O. Hazim, “A generalized knowledge- based short-term load forecasting technique,” IEEE Trans- actions on Power Systems, Vol. 8, pp. 508–514, 1993.
- D. K. Chaturvedi, M. Mohan, R. K. Singh, and P. K. Kalra, “Improved generalized neuron model for short term load forecasting,” International Journal on Soft Computing–A Fusion of Foundations, Methodologies and Applications, Springer–Verlag, Heidelberg, Vol. 8, No. 1, pp. 10–18, April 2004.
- G. Gross and F. D. Galiana, “Short term load fore- casting,” Proceedings of the IEEE, Vol. 75, No. 1212, pp. 1558–1573, December 1987.
- D. K. Chaturvedi, P. S. Satsangi, and P. K. Kalra, “New neuron models for simulating rotating electrical machines and load forecasting problems,” International Journal on Electric Power System Research, Elsevier Science, Ire- land, Vol. 52, pp. 123–131, 1999.
- P. S. Addision, “The illustrated wavelet transform hand- book: Introductory theory and applications in science, engineering medicine and finance,” IOP Publishing LTD, 2002.
- Z. Can, Z. Aslan, and O. Oguz, “One dimensional wave- let real analysis of gravity waves,” The Arabian Journal for Science and Engineering, Vol. 29, No. 2, pp. 33–42, 2004.
- Z. Can, Z. Aslan, O. Oguz, and A. H. Siddiqi, “Wavelet transform of meteorological parameters and gravity waves,” Annals Geophysics, Vol. 23, pp. 650–663, 2005.
- I. Daubechies, “Ten lectures on wavelets,” SIAM, Phila- delphia, 1992.
- A. F. Georgiou and P. Kumar, “Wavelet in geophysics,” Academic Press, San Diago, 1994.