DocumentCode :
2752317
Title :
Using Elman and FIR neural networks for short term electric load forecasting
Author :
Galarniotis, A.I. ; Tsakoumis, A.C. ; Fessas, P. ; Vladov, S.S. ; Mladenov, Valeri M.
Volume :
2
fYear :
2003
fDate :
0-0 2003
Firstpage :
433
Abstract :
Finite impulse response (FIR) neural network and Elman neural network have been compared in electric load prediction. An FIR neural network has been trained with a temporal back-propagation learning algorithm and the results obtained showed that the effectiveness of the algorithm is more important than the applied network model. The comparison between both networks and the standard approach with Multilayer perceptron (MLP) network, demonstrates that the FIR network acts adequately. It performs better than the Elman network. Both networks perform better than the MLP network.
Keywords :
backpropagation; load forecasting; multilayer perceptrons; neural nets; Elman neural network; FIR neural networks; MLP network; electric load forecasting; electric load prediction; finite impulse response; multilayer perceptron; short term load forecasting; temporal back-propagation learning algorithm; time series;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signals, Circuits and Systems, 2003. SCS 2003. International Symposium on
Print_ISBN :
0-7803-7979-9
Type :
conf
DOI :
10.1109/SCS.2003.1227082
Filename :
5731315
Link To Document :
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