By Topic

Short-Term Load Forecasting Using Artificial Neural Network Based on Particle Swarm Optimization Algorithm

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

2 Author(s)
Z. A. Bashir ; Dalhousie Univ., Halifax ; M. E. El-Hawary

The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. In this work, for determining the competitive learning model, the particle swarm optimization (PSO) technique is used as a training algorithm to adjust the weights of the artificial neural networks (ANNs) model to predict hourly loads. The feature of PSO is to fly potential solutions through hyperspace, accelerating toward better solutions. Thus the training phase should result in obtaining the weights configuration associated with the minimum output error. The historical load and weather information were trained and tested over a period of one season through two years. Generalized error estimation is done by using the reverse part of the data as a "test" set. The results were compared with conventional back-propagation algorithm and yielded encouraging results.

Published in:

2007 Canadian Conference on Electrical and Computer Engineering

Date of Conference:

22-26 April 2007