By Topic

Regularisation of neural networks for improved load forecasting in the power system

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
$33 $33
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

2 Author(s)
S. Osowski ; Warsaw Univ. of Technol., Poland ; K. Siwek

A regularisation procedure for neural-network reduction in order to obtain the best results for load forecasting in a power system is presented. The OBD pruning method was applied in the solution. The numerical experiments were concentrated on the prognosis of the load in the power system. Two kinds of experiments are described: a 24-hour forecast and the forecast of the daily mean of the load. It was shown that the application of the regularisation of the neural network employed for prediction resulted in a significant improvement of the forecasting accuracy

Published in:

IEE Proceedings - Generation, Transmission and Distribution  (Volume:149 ,  Issue: 3 )