By Topic

A real-time short-term load forecasting system using functional link network

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 $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

4 Author(s)
P. K. Dash ; Centre for Intelligent Syst., Regional Eng. Coll., Roukela, India ; H. P. Satpathy ; A. C. Liew ; S. Rahman

This paper presents a new functional-link network based short-term electric load forecasting system for real-time implementation. The load and weather parameters are modelled as a nonlinear ARMA process and parameters of this model are obtained using the functional approximation capabilities of an auto-enhanced functional link net. The adaptive mechanism with a nonlinear learning rule is used to train the link network on-line. The results indicate that the functional link net based load forecasting system produces robust and more accurate load forecasts in comparison to simple adaptive neural network or statistical based approaches. Testing the algorithm with load and weather data for a period of two years reveals satisfactory performance with mean absolute percentage error (MAPE) mostly less than 2% for a 24-hour ahead forecast and less than 2.5% for a 168-hour ahead forecast

Published in:

IEEE Transactions on Power Systems  (Volume:12 ,  Issue: 2 )