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IWANN
2005
Springer

Load Forecasting Using Fixed-Size Least Squares Support Vector Machines

10 years 7 months ago
Load Forecasting Using Fixed-Size Least Squares Support Vector Machines
Based on the Nystr¨om 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.
Marcelo Espinoza, Johan A. K. Suykens, Bart De Moo
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where IWANN
Authors Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor
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