Application of accurate online support vector regression in energy price forecast

Author

Zhou, Dianmin ; Gao, Feng ; Guan, Xiaohong

Author_Institution

Syst. Eng. Inst., Xi´´an Jiaotong Univ., China

Volume

2

fYear

2004

fDate

15-19 June 2004

Firstpage

1838

Abstract

Energy price is the most important indicator in electricity markets and its characteristics are related to the market mechanism and the change versus the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability. In this paper, an accurate online support vector regression (AOSVR) method is applied to update the price forecasting model. Numerical testing results show that the method is effective in forecasting the prices of the electric-power markets.

Keywords

power engineering computing; power markets; power system economics; pricing; regression analysis; support vector machines; accurate online support vector regression method; electric-power markets; electricity markets; energy price forecast; market indicator; real-time price forecasting model; Economic forecasting; Electricity supply industry; Load forecasting; Power engineering and energy; Power markets; Power system modeling; Predictive models; Systems engineering and theory; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on

Print_ISBN

0-7803-8273-0

Type

conf

DOI

10.1109/WCICA.2004.1340993

Filename

1340993