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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/765
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| Title: | Short term load forecasting using a novel recurrent neural network |
| Authors: | Mishra, Sanjib
Patra, S K |
| Keywords: | Short term load forecasting
recurrent neural network
computational intelligence |
| Issue Date: | 2008 |
| Citation: | International Journal for Computer Intelligence - Theory and Practice, Vol 3, No 2 |
| Abstract: | Short term load forecasting
is essential to the operation of electricity
companies. It enhances the energyefficient
and reliable operation of power
system. Neural networks (NNs) have
powerful nonlinear mapping
capabilities. Therefore, they have been
used to deal with predicting, in which
the conventional methods fail to give
satisfactory results. A novel Recurrent
neural network (RNN) is proposed in
this paper. Many types of computational
intelligent methods are available for
time series prediction. The novelty of
this RNN lies in the usage of neurons
instead of simple feedback loops for
temporal relations. There is flexibility to
use any type of activation functions in
both feed forward and feedback loops.
Number of hidden neurons can be
changed on case to case basis for
maximum accuracy. The performance
of the RNN is demonstrated to be better
than several other computational
intelligent methods available. |
| Description: | Copyright for the paper belongs to publishers |
| URI: | http://hdl.handle.net/2080/765 |
| Appears in Collections: | Journal Articles
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Size | Format |
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| Revised STLF using a proposed novel RNN.pdf | | 236Kb | Adobe PDF | View/Open |
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