Referenced Items are not available for this document.
Recently, deregulation has had a great impact on the electric power industry in various countries. Bidding competition is one of the main transaction approaches after deregulation. Locational marginal prices (LMPs) resulting from bidding competition signal electricity values at a node or in an area. This paper presents a method using recurrent neural networks (RNNs) for forecasting LMPs. These RNNs were trained/validated and tested with historical data from the PJM power system. It was found that the proposed neural networks are capable of forecasting LMP values efficiently
Published in:
Power Engineering Society Winter Meeting, 2001. IEEE
(Volume:2
)
Date of Conference: 2001
- Page(s):
- 539 - 544 vol.2
- Meeting Date :
- 28 Jan 2001-01 Feb 2001
- Print ISBN:
- 0-7803-6672-7
- INSPEC Accession Number:
- 7028239
- Conference Location :
- Columbus, OH
- Digital Object Identifier :
- 10.1109/PESW.2001.916905
- Product Type:
- Conference Publications
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.
