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
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;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1381093
