By Topic

Short-term electric load forecasting using neural network models

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)
Al-Rashid, Y. ; Dept. of Electr. Eng., Wichita State Univ., KS, USA ; Paarmann, L.D.

Short-term power load forecasting is used to provide utility company management with future information about electric load demand in order to assist them in running more economical and reliable day-to-day operations. An Artificial Neural Network (ANN) approach is used in this paper to construct a 24 hour ahead power load forecasting model for the winter and summer seasons. The proposed ANN models were tested by forecasting the electric load for the Wichita, Kansas, area throughout 1992. Then the forecasted results were compared to the actual load and the performance was evaluated and compared with that of a Time Series, ARMA, model

Published in:

Circuits and Systems, 1996., IEEE 39th Midwest symposium on  (Volume:3 )

Date of Conference:

18-21 Aug 1996