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A practical approach to electric load forecasting using artificial neural networks with corrective filtering

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3 Author(s)
L. D. Voss ; Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada ; M. M. A. Salama ; J. Reeve

This paper presents the practical application of an artificial neural network to the power system load forecasting problem. This work examines the training, testing, and operation of a simple neural network. Furthermore, a method for improving the prediction accuracy of a forecasting neural network is proposed. This approach views the forecasting problem as a knowledge-based discrete time filtering problem. Encouraging results have been obtained using this method for forecasting the peak monthly load of a power utility, over a number of years

Published in:

Electrical and Computer Engineering, 1995. Canadian Conference on  (Volume:1 )

Date of Conference:

5-8 Sep 1995