By Topic

Predicting China's Energy Consumption Using Artificial Neural Networks and Genetic Algorithms

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)
Shouchun Wang ; Sch. of Bus. Adm., China Univ. of Pet., Beijing, China ; Xiucheng Dong

In this work, artificial neural networks (ANN) based on genetic algorithm (GA) have been developed to predict energy consumption in China. The numbers of neurons in the hidden layer, the momentum rate and the learning rate are determined using the genetic algorithm. The inputs to the artificial neural networks model are four variables, namely, gross domestic product, industrial structure, total population and technology progress. It is verified that genetic algorithm could find the optimal architecture and parameters of the back-propagation algorithm. In addition, the artificial neural network model based genetic algorithm is tested and the results indicate that the energy consumption in China can be efficiently forecasted by this model. Compared with a network in which the ANN calibration is done using a trial-and-error approach, it can be found that this model can improve prediction accuracy.

Published in:

Business Intelligence and Financial Engineering, 2009. BIFE '09. International Conference on

Date of Conference:

24-26 July 2009