Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting

Authors:
M. Ulagammai;P. Venkatesh;P. S. Kannan;Narayana Prasad Padhy
Affiliations:
Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625 015, India;Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625 015, India;Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625 015, India;Department of Electrical Engineering, IIT Roorkee, Roorkee 247 667, India
Venue:
Neurocomputing
Year:
2007

Citing 2
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Abstract

A new load forecasting (LF) approach using bacterial foraging technique (BFT) trained wavelet neural network (WNN) is proposed in this paper. Artificial neural network (ANN) is combined with wavelet transform called wavelet neural network is applied for LF. The parameters of translation and dilation in the wavelet nodes and the weighting factors in the weighting nodes are tuned using BFT optimization. With the advantages of global search abilities of BFT as well as the multiresolution and localizing natures of wavelets, the networks are constructed which identifies the inherent non-linear characteristics of power system loads. The proposed approach is validated with Tamil Nadu Electricity Board (TNEB) system, India. The comparison of Delta Rule and BFT-based LF for different periods are depicted with their mean absolute percentage errors (MAPE).