Prediction of electricity consumption based on genetic algorithm - RBF neural network

Author

Qing-Wei, Zeng ; Zhi-Hai, Xu ; Jian, Wu

Author_Institution

Network Center, Nanchang Univ., Nanchang, China

Volume

5

fYear

2010

fDate

27-29 March 2010

Firstpage

339

Lastpage

342

Abstract

In order to avoid the economic loss due to too much or too little of electricity consumption, electricity consumption needs to be predicted. In order to solve the drawbacks of BP neural network, genetic algorithm and RBF neural network (GA-RBFNN) is presented to forecast electricity consumption in the study, and genetic algorithm is introduced and tried in optimizing the parameters of RBF neural network. The electricity consumption data and relevant features data of a certain province from September to December in 2007 are used as the experimental data. The experiment results indicate that GA-RBFNN is very suitable for electricity consumption prediction by relevant features data.

Keywords

backpropagation; genetic algorithms; power engineering computing; power system economics; radial basis function networks; BP neural network; RBF neural network; economic loss; electricity consumption data; electricity consumption prediction; genetic algorithm; Biological cells; Economic forecasting; Electronic mail; Energy consumption; Feedforward neural networks; Genetic algorithms; Neural networks; Predictive models; Temperature; Weather forecasting; RBF neural network; electricity consumption; genetic algorithm; prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computer Control (ICACC), 2010 2nd International Conference on

Conference_Location

Shenyang

Print_ISBN

978-1-4244-5845-5

Type

conf

DOI

10.1109/ICACC.2010.5487062

Filename

5487062