Wavelet neural network based short term load forecasting of electric power system commercial load

Author

Oonsivilai, Anant ; El-Hawary, M.E.

Author_Institution

Dept. of Electr. & Comput. Eng., Dalhousie Univ., Halifax, NS, Canada

Volume

3

fYear

1999

fDate

9-12 May 1999

Firstpage

1223

Abstract

In an electric power system, the system load consists of domestic, commercial, industrial and municipal load sectors. This paper presents an approach for predicting electric power system commercial load using a wavelet neural network. Morlet and Mexican hat wavelets are used to generate the transfer functions of hidden layer nodes of the neural network. A wavelet neural network is trained for a particular power system load. Results show that wavelet neural networks may outperform traditional architectures in approximation and forecasting problems related to electric power system.

Keywords

learning (artificial intelligence); load forecasting; neural nets; power system analysis computing; transfer functions; wavelet transforms; Mexican hat wavelets; Morlet wavelets; commercial load sector; domestic load sector; electric power system commercial load; hidden layer nodes; industrial load sector; municipal load sector; neural network; neural network training; short term load forecasting; transfer functions; wavelet neural network; Artificial intelligence; Artificial neural networks; Expert systems; Load forecasting; Neural networks; Neurons; Power system modeling; Power system security; Predictive models; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 1999 IEEE Canadian Conference on

Conference_Location

Edmonton, Alberta, Canada

ISSN

0840-7789

Print_ISBN

0-7803-5579-2

Type

conf

DOI

10.1109/CCECE.1999.804865

Filename

804865