Short-Term Load Forecasting Using Soft Computing Techniques
D. K. Chaturvedi, Sinha Anand Premdayal, Ashish Chandiok
DOI: 10.4236/ijcns.2010.33035   PDF    HTML     6,559 Downloads   13,565 Views   Citations


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.

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D. Chaturvedi, S. Premdayal and A. Chandiok, "Short-Term Load Forecasting Using Soft Computing Techniques," International Journal of Communications, Network and System Sciences, Vol. 3 No. 3, 2010, pp. 273-279. doi: 10.4236/ijcns.2010.33035.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] G. Gross and F. D. Galiana, “Short term load fore- casting,” Proceedings of the IEEE, Vol. 75, No. 1212, pp. 1558–1573, December 1987.
[8] 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.
[9] P. S. Addision, “The illustrated wavelet transform hand- book: Introductory theory and applications in science, engineering medicine and finance,” IOP Publishing LTD, 2002.
[10] 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.
[11] 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.
[12] I. Daubechies, “Ten lectures on wavelets,” SIAM, Phila- delphia, 1992.
[13] A. F. Georgiou and P. Kumar, “Wavelet in geophysics,” Academic Press, San Diago, 1994.
[14] K. M. Furati, M. Z. Nashed, and A. H. Siddiqi, “Mathe- matical models and methods for real world and systems,” Chapman and Hall/CRC, Taylor and Francis Group, Boca Raton, London, New York, Singapore, 2006.
[15] Z. Z. Hu and T. Nitta, “Wavelet analysis of summer rain- fall over north China and India,” Journal Meteorological Society of Japan, Vol. 74, No. 6, 1996.
[16] J. S. R. Jang and N. Gulley, “Fuzzy logic toolbox,” The Mathworks Inc., 24 Prime Park Way, Natick, Mass, 1995.
[17] J. S. R. Jang and C. T. Sun, “Neuro-fuzzy modeling and control,” Proceedings of the IEEE, Vol. 83, No. 3, pp. 378–406, 1995.
[18] A. K. Mahalanabis, D. P. Kothari, and S. I. Ahson, “Computer aided power system analysis and control,” Tata McGraw Hill Publishing Company Limited, New Delhi, 1988.
[19] G. E. Box and G. M. Jenkins, “Time series analysis: Forecasting and control,” Holden-Day, San Fransisco, 1976.
[20] I. Moghram and S. Rahman “Analysis and evaluation of five short-term load forecasting techniques,” IEEE Trans- actions on Power Systems, Vol. 4, pp. 1484–1491, Oct- ober 1989.
[21] C. H. Chen, “Fuzzy logic and neural network handbook,” McGraw Hill Computer Engineering, 1996.
[22] G. J. Klis and T. A. Folger, “Fuzzy sets uncertainty and information,” Prentice Hall of India Private Limited, 1993.
[23] P. Kumar and E. Foufoula–Georgiou, “Wavelet analysis for geophysical applications,” Reviews of Geophysics, Vol. 33, pp. 385–412, 1997.
[24] S. Mallat, “A wavelet tour of signal processing,” Acade- mic Press, NewYork, 1998.
[25] P. Manchanda, J. Kumar, and A. H. Siddiqi, “Mathema- tical methods for modeling price fluctuations of financial time series,” Journal of Franklin Institute, Vol. 344, pp. 613–636, 2007.
[26] A. H. Siddiqi, “Applied functional analysis,” Maral Dek- kar, New York, 2004.
[27] A. H. Siddiqi, G. Kovin, W. Freeden, U. Mosco, and S. Stephan, “Theme issue on wavelet fractal in science and engineering,” Arabian Journal for Science and Engi- neering, KFUPM.
[28] O. Stanislaw and K. Garanty, “Forecasting of the daily meteorological pollution using wavelet and support vector machine,” Engineering Application of Artificial Intelligence, Vol. 20, pp. 745–755, 2007.
[29] S. Yousefi, I. Weinrich, and D. Reinarz–Chaos, “Wavelet -based prediction of oil prices,” Solution and Fractals, Vol. 25, pp. 265–275, 2005.
[30] M. V. Wickerhauser, A. K. Peters, and Wellesley, “Adap- ted analysis from theory to software,” 1994.

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