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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.

has been cited by the following article:

• TITLE: Short-Term Load Forecasting Using Soft Computing Techniques

  AUTHORS: D. K. Chaturvedi, Sinha Anand Premdayal, Ashish Chandiok

  KEYWORDS: Wavelet Transform, Short Term Load Forecasting, Soft Computing Techniques

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.3 No.3, March 31, 2010

  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.