Title :
Regularization of neural networks for improved load forecasting in power system
Author :
Siwek, Krzysztof ; Osowski, Stanislaw
Author_Institution :
Warsaw Univ. of Technol., Poland
fDate :
6/23/1905 12:00:00 AM
Abstract :
Presents the regularization procedure for the neural network reduction to obtain the best results of load forecasting in the power system. The OBD pruning method will be applied in the solution. The numerical experiments have been concentrated on the prognosis of the load in the power system. Two kinds of experiments are described: 24-hour forecast and the forecast of the daily mean of the load. It will be shown that application of the regularization of the neural network employed for prediction will result in significant improvement of the forecasting accuracy
Keywords :
load forecasting; neural nets; power system simulation; 24 hr; 24-hour forecast; OBD pruning method; daily mean; forecasting accuracy; load forecasting; neural networks; optimal brain damage; power system; regularization procedure; Biological neural networks; Cost function; Feedforward neural networks; Intelligent networks; Load forecasting; Neural networks; Paper technology; Power systems; Technology forecasting; Training data;
Conference_Titel :
Electronics, Circuits and Systems, 2001. ICECS 2001. The 8th IEEE International Conference on
Print_ISBN :
0-7803-7057-0
DOI :
10.1109/ICECS.2001.957443
