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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/763

Title: Short term load forecasting using a novel recurrent neural network
Authors: Mishra, Sanjib
Patra, S K
Keywords: Short term load forecasting
computational intelligence
recurrent neural network
Issue Date: 2008
Publisher: IEEE
Citation: TENCON 2008. IEEE Region 10 Conference 19-21 Nov. 2008 Page(s):1 - 6
Abstract: Short term load forecasting is essential to the operation of electricity companies. It enhances the energy-efficient 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 IEEE
URI: http://dx.doi.org/10.1109/TENCON.2008.4766829
http://hdl.handle.net/2080/763
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