Name
Title | ||
|---|---|---|
Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting |
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). |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.neucom.2006.05.020 | Neurocomputing |
Keywords | Field | DocType |
wavelet neural network,load forecasting,artificial neural network,bft-based lf,weighting node,weighting factor,bacterial foraging technique,trained wavelet neural network,wavelet node,bft optimization,power system load,mean absolute percentage error,wavelet transform,power system | Delta rule,Weighting,Pattern recognition,Electric power system,Load forecasting,Artificial intelligence,Artificial neural network,Foraging,Machine learning,Mathematics,Wavelet,Wavelet transform | Journal |
Volume | Issue | ISSN |
70 | 16-18 | Neurocomputing |
Citations | PageRank | References |
31 | 2.37 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Ulagammai | 1 | 31 | 2.37 |
| Venkatesh, P. | 2 | 45 | 4.34 |
| P. S. Kannan | 3 | 81 | 6.07 |
| P. Prasad | 4 | 143 | 15.96 |