Title: Load forecasting using fixed-size Least Squares Support Vector Machines
Authors: Espinoza, M ×
Suykens, Johan
De Moor, Bart #
Issue Date: 2005
Publisher: Springer
Series Title: Lecture Notes in Computer Science vol:3512 pages:1018-1026
Conference: International World-conference on Artificial Neural Networks
Abstract: Based on the Nystrom approximation and the primal-dual formulation of Least Squares Support Vector Machines (LS-SVM), it becomes possible to apply a nonlinear model to a large scale regression problem. This is done by using a sparse approximation of the nonlinear mapping induced by the kernel matrix, with an active selection of support vectors based on quadratic Renyi entropy criteria. The methodology is applied to the case of load forecasting as an example of a real-life large scale problem in industry, for the case of 24-hours ahead predictions. The results are reported for different number of initial support vectors, which cover between 1% and 4% of the entire sample, with satisfactory results.
Description: \emph{Computational Intelligence and Bioinspired Systems}, (Cabestany J., Prieto A., and Sandoval F., eds.), Proceedings of the 8th International Work-Conference on Artificial Neural Networks, vol. 3512 of \emph{Lecture Notes in Computer Science}, Springer-Verlag, 2005
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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