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Short-Term Load Forecasting Using Soft Computing Techniques

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DOI: 10.4236/ijcns.2010.33035    5,744 Downloads   11,960 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

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.

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