Title :
Training sample dimensions impact on artificial neural network optimal structure
Author :
Manusov, V.Z. ; Makarov, I.S. ; Dmitriev, S.A. ; Eroshenko, S.A.
Author_Institution :
Dept. of Power Supply of the Enterprises, Novosibirsk State Tech. Univ., Novosibirsk, Russia
Abstract :
The paper addresses the problem of electric load forecasting, using artificial neural networks mathematical apparatus, subject to error minimization on the long forecasting interval. Balanced artificial neural network architecture gives the possibility to maintain small deviation between forecasted and real values simultaneously with constrained squared error variation maintenance. Proposed methodology was verified using real data.
Keywords :
learning (artificial intelligence); load forecasting; neural net architecture; power engineering computing; artificial neural network mathematical apparatus; artificial neural network optimal structure; balanced artificial neural network architecture; constrained squared error variation maintenance; electric load forecasting problem; error minimization; sample dimension impact training; Artificial neural networks; Biological neural networks; Energy consumption; Forecasting; Training; Vectors; artificial neural network; forecasting; neural network training;
Conference_Titel :
Environment and Electrical Engineering (EEEIC), 2013 12th International Conference on
Conference_Location :
Wroclaw
Print_ISBN :
978-1-4673-3060-2
DOI :
10.1109/EEEIC.2013.6549608
