Hybrid load forecasting method based on fuzzy support vector machine and linear extrapolation

Author

Jiang, Xin ; Liu, Xiao-Hua ; Gao, Rong

Author_Institution

Sch. of Math. & Inf., Ludong Univ., Yantai, China

fYear

2012

fDate

6-8 July 2012

Firstpage

2431

Lastpage

2435

Abstract

For the load affected by many factors and near the far smaller feature, a hybrid load forecasting method based on fuzzy support vector machine and linear extrapolation is proposed. The similar day is selected by the integrated effects of meteorology and time, and the fuzzy membership of the corresponding training sample is obtained by normalized similarity. Using the fuzzy support vector machine to predict the maximum and minimum loads of the forecasting day, then the load is combined with the load curve trend obtained by the linear extrapolation based on the similar day. The simulation results show that the proposed method can improve the predicting accuracy.

Keywords

extrapolation; fuzzy set theory; hybrid power systems; load forecasting; power engineering computing; support vector machines; fuzzy membership; fuzzy support vector machine; hybrid load forecasting method; linear extrapolation; load curve trend; maximum load prediction accuracy improvement; meteorology; minimum load prediction accuracy improvement; normalized similarity; power system; training sample; Educational institutions; Extrapolation; Humidity; Load forecasting; Market research; Support vector machines; Temperature sensors; fuzzy support vector machine; linear extrapolation; load forecasting; power system; similar day;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2012 10th World Congress on

Conference_Location

Beijing

Print_ISBN

978-1-4673-1397-1

Type

conf

DOI

10.1109/WCICA.2012.6358281

Filename

6358281