Application of accurate online support vector regression in energy price forecast
Dianmin Zhou; Feng Gao; Xiaohong Guan
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Volume 2, Issue , 15-19 June 2004 Page(s): 1838 - 1842 Vol.2
Digital Object Identifier   10.1109/WCICA.2004.1340993
Summary: 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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