Application of fuzzy support vector machines in short-term load forecasting

Authors:
Yuancheng Li;Tingjian Fang
Affiliations:
Department of Automation, University of Science and Technology of China, HeFei, P.R. China;Institute of Intelligent Machines, Academia Sinica, HeFei, P.R.China
Venue:
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Year:
2003

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Abstract

A new method using Fuzzy Support Vector Machines (FSVM) is presented for Short-Term Load Forecasting (STLF). In many regression problems, the effects of the training points are different. It is often that some training points are more important than others. In FSVM, we apply a fuzzy membership to each input point such that different input points can make different contributions to the learning of decision surface. The results of experiment indicate that FSVM is effective in improving the accuracy of STLF.