Electricity Consumption Forecasting Based on Improved BP Neural Network

Author

Zhang Xing-ping ; Yuan Jia-hai

Author_Institution

Sch. of Bus. Adm., North China Power Electr. Univ., Beijing

fYear

2008

fDate

4-6 Nov. 2008

Firstpage

357

Lastpage

360

Abstract

An improved BP Neural Network with additional momentum and adaptive learning is proposed in the paper to predict the growth rate of electricity consumption in China. Matlab7 is used as modeling tool to design the model. Current year GDP growth, electric power consumption growth and growth rate of secondary industry are taken as input variables while next year electric power consumption growth is predicted. The simulation results are compared with that of traditional BP Neural Network model, which show the feasibility of the model proposed in the paper.

Keywords

backpropagation; economic indicators; energy consumption; load forecasting; neural nets; power engineering computing; power generation economics; BP neural network; China; GDP growth; adaptive learning; electricity consumption forecasting; momentum learning; Artificial neural networks; Computer languages; Economic forecasting; Economic indicators; Energy consumption; Mathematical model; Neural networks; Neurons; Research and development management; Risk management; Adaptive learning; Electricity demand; Learning algorithm; Neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Risk Management & Engineering Management, 2008. ICRMEM '08. International Conference on

Conference_Location

Beijing

Print_ISBN

978-0-7695-3402-2

Type

conf

DOI

10.1109/ICRMEM.2008.104

Filename

4673255