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Proceedings Paper

Neural network learning algorithms for electric load forecasting
Author(s): Emil Pelikan; Vaclav Sebesta
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Paper Abstract

In this contribution the special interest is focused on the prediction error distributions using feedforward multilayer neural network predictors. The procedure of prediction can be evaluated not only with respect to the mean absolute (or mean square) error but the frequency of higher errors are also taken into account. Two new modifications of the learning algorithms are suggested. The first one is based on error controlled input sample selection for efficient training. The second one is based on the minimization of the special criterial functions, which more reflect especially the great deviations. The functions are expressed in the form of minimax criterial function or in the form of weighted sum of higher order deviations between predicted and measured values. The classical backpropagation is used in the first case and the stochastical method of the statistical gradient is used in the second case. The efficiency of our approaches is demonstrated on the electric load forecasting problem in the west bohemian region in the Czech Republic. A reduction in frequency of higher errors in the everyday morning peaks forecasting were achieved.

Paper Details

Date Published: 2 March 1994
PDF: 11 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.170001
Show Author Affiliations
Emil Pelikan, Institute of Computer Science (Czech Republic)
Vaclav Sebesta, Institute of Computer Science (Czech Republic)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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