Cart (Loading....) | Create Account
Close category search window
 

Forecasting annual electricity demand using BP neural network based on three sub-swarms PSO

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)
Ruiyou Zhang ; Inst. of Syst. Eng., Northeastern Univ., Shenyang ; Dingwei Wang

Forecast of annual electricity demand is very important for the market settlement and transmission pricing of power system. Therefore, a forecasting model combing back propagation (BP) neural network and three sub-swarms particle swarm optimization (THSPSO) is proposed. Some important economical factors of the year to be forecasted, such as the gross product, the population, the price index, and so on, are considered in the forecast model. On the other hand, annual electricity demands are considered as a time series. Firstly, the weights and bias of the neural network if globally optimized based on THSPSO, which has a stronger diversification than the basic PSO. Secondly, the network is trained by BP algorithm with the obtained values from THSPSO as the initial values. The case study of Liaoning Province of China indicates that the network can be trained quickly by the hybrid algorithm of THSPSO and BP, and that annual electricity demand can be forecasted by this network with high precision.

Published in:

Control and Decision Conference, 2008. CCDC 2008. Chinese

Date of Conference:

2-4 July 2008

Need Help?


IEEE Advancing Technology for Humanity 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 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.