Electric Load Forecasting using SVMS

Author

Guo, Xin-Chen ; Liang, Yan-Chun ; Wu, Chun-Guo ; Wang, Hao-yong

Author_Institution

Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

4213

Lastpage

4215

Abstract

Support vector machines (SVMs) have been proposed as a novel technique and applied to regression recently. In this paper, SVMS are used for load forecasting. The training sample sets are chosen and preprocessed before every forecasting. Then the interference of the non-correlative and bad samples for the forecasting can be avoided. The effectiveness and the feasibility of forecasting of the employed method are examined through some simulations

Keywords

load forecasting; power engineering computing; regression analysis; support vector machines; electric load forecasting; noncorrelative interference; regression method; support vector machine; Artificial intelligence; Cybernetics; Economic forecasting; Educational institutions; Educational technology; Fuzzy logic; Knowledge engineering; Laboratories; Load forecasting; Machine learning; Predictive models; Support vector machines; Support vector machine; load forecasting; regression approximation;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258945

Filename

4028811

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2899666