Application of accurate online support vector regression in energy price forecast
Dianmin Zhou
Feng Gao
Xiaohong Guan
Syst. Eng. Inst., Xi'an Jiaotong Univ., China;
This paper appears in: Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Publication Date: 15-19 June 2004
Volume: 2,
On page(s): 1838- 1842 Vol.2
ISSN:
ISBN: 0-7803-8273-0
INSPEC Accession Number: 8169140
Digital Object Identifier: 10.1109/WCICA.2004.1340993
Current Version Published: 2004-10-18
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.
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