Short-Term Load Forecasting Based on Wavelet Neural Network and Monkey-King Genetic Algorithm

Author

Li, Shuangchen ; Yan, Ying ; Lin, Yufang

Author_Institution

North China Electr. Power Univ., Baoding, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

This paper presents a short-term load forecasting method based on wavelet neural network (WNN) and monkey-king genetic algorithm (MK). Parameters of WNN are mostly selected artificially or obtained through experiment time after time. A certain and effective method has not been found. Aiming at solving this problems, a method optimizing the WNN parameters with monkey-king genetic algorithm (MKWNN) was presented. The simulation results show that the proposed method possesses high forecasting accuracy and adaptability.

Keywords

genetic algorithms; load forecasting; neural nets; power engineering computing; wavelet transforms; monkey-king genetic algorithm; short-term load forecasting; wavelet neural network; Artificial neural networks; Economic forecasting; Genetic algorithms; Genetic mutations; Load forecasting; Neural networks; Optimization methods; Power generation economics; Wavelet analysis; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5365279

Filename

5365279