A novel genetic-algorithm-based neural network for short-term load forecasting

Author

Ling, S.H. ; Leung, Frank H F ; Lam, H.K. ; Lee, Yim-Shu ; Tam, Peter K S

Author_Institution

Dept. of Electron. & Inf. Eng., Hong Kong Polytech. Univ., Kowloon, China

Volume

50

Issue

4

fYear

2003

Firstpage

793

Lastpage

799

Abstract

This paper presents a neural network with a novel neuron model. In this model, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. This neural network provides better performance than a traditional feedforward neural network, and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by a genetic algorithm with arithmetic crossover and nonuniform mutation. Some applications are given to show the merits of the proposed neural network.

Keywords

feedforward neural nets; genetic algorithms; load forecasting; power system analysis computing; activation functions; arithmetic crossover; feedforward neural network; genetic algorithm; genetic-algorithm-based neural network; hidden layer; neuron model; node-to-node relationship; nonuniform mutation; short-term load forecasting; Arithmetic; Backpropagation algorithms; Feedforward neural networks; Genetic algorithms; Genetic mutations; Load forecasting; Neural networks; Neurons; Pattern recognition; Senior members;

fLanguage

English

Journal_Title

Industrial Electronics, IEEE Transactions on

Publisher

ieee

ISSN

0278-0046

Type

jour

DOI

10.1109/TIE.2003.814869

Filename

1215484