Short-term load forecasting using multiple support vector machines based on fuzzy clustering

Author

Gaorong ; Xiao-hua, Liu

Author_Institution

Shool of Math. & Inf., LuDong Univ., Yantai, China

fYear

2009

fDate

17-19 June 2009

Firstpage

3183

Lastpage

3186

Abstract

According to the future of power load, a load forecasting method of multiple support vector machine based on fuzzy clustering is proposed. Data type, weather and temperature factors are considered in the model. Load data are classified using fuzzy clustering. Each class was modeled using support vector machines which best fitted the special class. The method was simulated utilizing the load data of Shan Dong electrical company from 2005 to 2007. The simulation result showed our method can improve the forecasting accuracy.

Keywords

environmental factors; fuzzy set theory; load forecasting; power engineering computing; support vector machines; Shan Dong electrical company; data type; fuzzy clustering; multiple support vector machines; short-term load forecasting; temperature factors; weather factors; Load forecasting; Mathematics; Predictive models; Risk management; Support vector machine classification; Support vector machines; Temperature; Weather forecasting; fuzzy clustering; short-term load forecasting; support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5192735

Filename

5192735