Cart (Loading....) | Create Account
Close category search window

Neural network-based short term load forecasting for unit commitment scheduling

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

5 Author(s)
Methaprayoon, K. ; Energy Syst. Res. Center, Texas Univ., Arlington, TX, USA ; Lee, W.J. ; Didsayabutra, P. ; Liao, J.
more authors

Today's electric power industry is undergoing many fundamental changes due to the process of deregulation. In the new market environment, the power system operation will become more competitive. The utilities are required to perform optimal planning in order to operate their system efficiently. The accuracy of future load forecast becomes crucial. This paper presents the development of an artificial neural network-based short-term load forecasting (STLF) for unit commitment scheduling and resource planning. The network structures are carefully tuned to obtain satisfying forecast results according to the load characteristics of the target utility system. The result indicates that ANN forecaster provides more accurate result and can be modified to satisfy the target utility's requirement.

Published in:

Industrial and Commercial Power Systems, 2003. 2003 IEEE Technical Conference

Date of Conference:

4-8 May 2003

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.