Electricity price short-term forecasting using artificial neural networks

Author

Szkuta, B.R. ; Sanabria, L.A. ; Dillon, T.S.

Author_Institution

Appl. Comput. Res. Inst., La Trobe Univ., Melbourne, Vic., Australia

Volume

14

Issue

3

fYear

1999

fDate

8/1/1999 12:00:00 AM

Firstpage

851

Lastpage

857

Abstract

This paper presents the system marginal price (SMP) short-term forecasting implementation using the artificial neural networks (ANN) computing technique. The described approach uses the three-layered ANN paradigm with backpropagation. The retrospective SMP real-world data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The results presented in this paper confirm considerable value of the ANN based approach in forecasting the SMP

Keywords

backpropagation; multilayer perceptrons; neural nets; power system analysis computing; power system economics; tariffs; Australia; Victoria; artificial neural nets; backpropagation; deregulated power systems; electricity price short-term forecasting; system marginal price; three-layered ANN paradigm; training; Artificial neural networks; Computer networks; Economic forecasting; Electricity supply industry; Electricity supply industry deregulation; Forward contracts; Load forecasting; Power generation; Power system modeling; Power systems;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.780895

Filename

780895