DocumentCode
2327947
Title
Electric load demand prediction using neural network trained by Kalman filtering
Author
Sanchez, Edgar N. ; Alanis, Alma Y. ; Rico, Jesus
Author_Institution
CINVESTAV, Guadalajara, Mexico
Volume
4
fYear
2004
fDate
25-29 July 2004
Firstpage
2771
Abstract
This work presents the application of recurrent multilayer perceptron neural networks to electric load demand prediction; the respective training is performed extended Kalman filtering. The goal is to obtain a 24 hours horizon, prediction for the electric load demand; data from the state of California, USA, is utilized.
Keywords
Kalman filters; load forecasting; multilayer perceptrons; nonlinear filters; power engineering computing; recurrent neural nets; electric load demand prediction; extended Kalman filtering; recurrent multilayer perceptron neural networks; Additive white noise; Costs; Filtering algorithms; Kalman filters; Multi-layer neural network; Multilayer perceptrons; Neural networks; Power system reliability; Recurrent neural networks; State estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-8359-1
Type
conf
DOI
10.1109/IJCNN.2004.1381093
Filename
1381093
Link To Document