Title: Short term load forecasting by a modified backpropagation trained neural network
Creator: Barnard, S. J.
Subject: Neural networks (Computer science), Algorithms., Machine learning
Date: 2012-08-15
Type: Thesis
Identifier: uj:9391
Identifier: http://hdl.handle.net/10210/5828
Description: M. Ing., This dissertation describes the development of a feedforwa.rd neural network, trained by means of an accelerated backpropagation algorithm, used for the short term load forecasting on real world data. It is argued that the new learning algorithm. I-Prop, - is a faster training - algorithm due to the fact that the learning rate is optimally predicted and changed according to a more efficient formula (without the need for extensive memory) which speeds up the training process. The neural network developed was tested for the month of December 1994, specifically to test the artificial neural network's ability to correctly predict the load during a Public Holiday, as well as the change over from Public Holiday to 'Normal' working day. In conclusion, suggestions are made towards further research in the improvement of the I-Prop algorithm as well as improving the load forecasting technique implemented in this dissertation.
Contributor: Dr. K.J. Nel.
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Short term load forecasting by a modified backpropagation trained neural network