By Topic

Short-term system marginal price forecasting with hybrid module

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

2 Author(s)

In this paper, dynamic clustering and a BP neural network were combined to forecast short-term system marginal price (SMP). With the criterion of minimal distance between the sample data and the clustering center, sample data were divided into several classes with the dynamic clustering method. Then BP neural network modules with the same topology structure and different weights and thresholds were built for every class. Weights and thresholds of different layers' neurons in the BP neural network were revised in the backpropagation process. This set of forecasting modules were trained and tested on historical marginal price data from the American PJM power system. This kind of module can correctly predict short-term system marginal price.

Published in:

Power System Technology, 2002. Proceedings. PowerCon 2002. International Conference on  (Volume:4 )

Date of Conference: