Short term load forecasting by using wavelet neural networks

Author

Bashir, Zidan ; El-Hawary

Author_Institution

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

Volume

1

fYear

2000

fDate

2000

Firstpage

163

Abstract

The application of the wavelet neural networks (WNNs) to short-term load forecasting is reported in this work. The wavelet neural network has much higher ability of generalization and fast convergence for learning than a multilayer feedforward neural network. The Morlet wavelet has been chosen in this study as the activation function. The 3-layer backpropagation algorithm is used to train the network by learning the nonlinear relationship between input and output of the network. The input data consists of historical load and weather information, which are collected over a period of 2-years (1994-1995) to train the network and data of one year (1996) is used to test the network. The results of the network have been compared with an artificial neural network and show an improved forecast with fast convergence

Keywords

backpropagation; load forecasting; neural nets; power system analysis computing; wavelet transforms; 3-layer backpropagation algorithm; Morlet wavelet; convergence; historical load information; multilayer feedforward neural network; network training; short term load forecasting; wavelet neural networks; weather information; Artificial neural networks; Convergence; Economic forecasting; Feedforward neural networks; Least squares approximation; Load forecasting; Multi-layer neural network; Neural networks; Wavelet analysis; Weather forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 2000 Canadian Conference on

Conference_Location

Halifax, NS

ISSN

0840-7789

Print_ISBN

0-7803-5957-7

Type

conf

DOI

10.1109/CCECE.2000.849691

Filename

849691