An artificial neural network for short term load forecasting

Author

Xiao, Benzheng ; McLaren, Peter G.

Author_Institution

Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada

Volume

1

fYear

1995

fDate

15-16 May 1995

Firstpage

129

Abstract

This paper presents the implementation of an artificial neural network (ANN) based short-term load forecasting model for the Manitoba Hydro power system, Canada. The selection of input variables and their transformations, the choice of the neural network architecture, the collection of training samples and training process are discussed in this paper. The results of the proposed neural network are very encouraging. The architecture of the proposed network is very simple, takes less time to train, and it is easy to use for reforecasting. The weights in the network show the relationship between the load and the weather conditions

Keywords

feedforward neural nets; learning (artificial intelligence); load forecasting; power system analysis computing; artificial neural network; input variables selection; input variables transformation; power systems; short-term load forecasting; training process; training samples; weights; Artificial neural networks; Expert systems; Input variables; Load forecasting; Load modeling; Neural networks; Neurons; Predictive models; State-space methods; Weather forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

WESCANEX 95. Communications, Power, and Computing. Conference Proceedings., IEEE

Conference_Location

Winnipeg, Man.

Print_ISBN

0-7803-2725-X

Type

conf

DOI

10.1109/WESCAN.1995.493958

Filename

493958