Referenced Items are not available for this document.
Electric power utilities require forecast of system demand or electrical load for one to seven days ahead. This paper studies a short-term electric load forecasting technique using a multi-layered feedforward artificial neural network (ANN) and a fuzzy set-based classification algorithm. The hourly data is subdivided into various class of weather conditions using the fuzzy set representation of weather variables and then the ANN's are trained and used to perform the load forecasting up to 120 hours ahead with a remarkable accuracy
Published in:
Power Systems, IEEE Transactions on
(Volume:13
,
Issue:
4
)
Date of Publication: Nov 1998
- Page(s):
- 1386 - 1391
- ISSN :
- 0885-8950
- INSPEC Accession Number:
- 6080351
- Digital Object Identifier :
- 10.1109/59.736281
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Nov 1998
- Sponsored by :
- IEEE Power & Energy Society
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 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
