Neural network learning algorithms for electric load forecasting

Emil Pelikan; Vaclav Sebesta

doi:10.1117/12.170001

2 March 1994 Neural network learning algorithms for electric load forecasting

Emil Pelikan, Vaclav Sebesta

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.170001
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

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.

Citation Download Citation

Emil Pelikan and Vaclav Sebesta "Neural network learning algorithms for electric load forecasting", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.170001

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