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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chun-Fu Lin, Sheng-De Wang, Fuzzy Support Vector Machines, IEEE Trans on Neural Networks. Vol. 13 No. 2 March (2002) 464–471
V. N. Vapnik, Statistical Learning Theory. New York: Wiley (1998)
B. Schölkopf, C. Burges, and A. Smola, Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Press (1999)
Smola, A.J. Regression Estimation with Support Vector Learning Machines. Master’s thesis, Technische Universität München (1996)
Mukherjee S., Osuna E., Girosi F. Nonlinear prediction of chaotic time series using support vector machines. Proceedings of IEEE NNSP’ 97, Amelia Island, FL (1997)
Smola, A. and B. Scholköpf, A tutorial on support vector regression, NeuroCOLT Tech. Rep. TR 1998-030, Royal Holloway College, London, U.K (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Y., Fang, T. (2003). Application of Fuzzy Support Vector Machines in Short-Term Load Forecasting. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_58
Download citation
DOI: https://doi.org/10.1007/3-540-39205-X_58
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-14040-5
Online ISBN: 978-3-540-39205-7
eBook Packages: Springer Book Archive