By Topic

Training data sensitivity problem of artificial neural network-based power system load forecasting

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ma, H. ; Dept. of Electr. Eng., Alabama Univ., Tuscaloosa, AL, USA ; El-Keib, A.A. ; Ma, X.

A crucial problem with the artificial neural network-based load forecasting is that its forecasting performance is significantly affected by the selection of training data used to calculate the network weights. The inherent shortcoming of this approach is verified through a typical example presented in this paper. Test results show that the short-term load forecasting error is very sensitive to the amplitude of the noise signal which is added to a portion of the training data. The presented test cases approximately simulate the load conditions during abrupt weather changing periods. Possible strategies to remedy this problem are also discussed in the paper

Published in:

System Theory, 1994., Proceedings of the 26th Southeastern Symposium on

Date of Conference:

20-22 Mar 1994