Discrete Dynamics in Nature and Society
Volume 2011 (2011), Article ID 597634, 12 pages
Research Article

Application of Chaos and Neural Network in Power Load Forecasting

School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China

Received 8 March 2011; Accepted 16 June 2011

Academic Editor: Recai Kilic

Copyright © 2011 Li Li and Liu Chong-xin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper employs chaos theory into power load forecasting. Lyapunov exponents on chaos theory are calculated to judge whether it is a chaotic system. Delay time and embedding dimension are calculated to reconstruct the phase space and determine the structure of artificial neural network (ANN). Improved back propagation (BP) algorithm based on genetic algorithm (GA) is used to train and forecast. Finally, this paper uses the load data of Shaanxi province power grid of China to complete the short-term load forecasting. The results show that the model in this paper is more effective than classical standard BP neural network model.