Title :
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
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;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258945
