Application of two-dimensional support vector machine in short-term Load forecasting

Author

Yang, Jingfei ; Stenzel, Jürgen

Author_Institution

Darmstadt Univ. of Technol., Darmstadt

fYear

2005

fDate

27-30 June 2005

Firstpage

1

Lastpage

4

Abstract

In this paper, the short-term load forecasting for the power system with heavy impulse loads is explored. In order to eliminate the effect of random startup and shutdown of high- power motors to the load prediction, support vector machine is applied to smooth the load curve and get the essential load. Second order derivative method is employed to find outliers and replace them with a reasonable value. With the essential load, support vector machine is applied again to train the data and predict the future load. The effectiveness of the proposed method is demonstrated by its application to a practical power system.

Keywords

load forecasting; power engineering computing; power systems; support vector machines; high- power motors; power system; second order derivative method; short-term load forecasting; two-dimensional support vector machine; Load forecasting; Metals industry; Power industry; Power system modeling; Power system security; Power systems; Predictive models; Smoothing methods; Support vector machines; Training data; impulse load; outlier; short-term load forecasting; smoothing; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Tech, 2005 IEEE Russia

Conference_Location

St. Petersburg

Print_ISBN

978-5-93208-034-4

Electronic_ISBN

978-5-93208-034-4

Type

conf

DOI

10.1109/PTC.2005.4524382

Filename

4524382