Fixed-size least squares support vector machines: A large scale application in electrical load forecasting

被引：74

作者：

Espinoza M. ^{[1
]}

Suykens J.A.K. ^{[1
]}

De Moor B. ^{[1
]}

机构：

[1] ESAT/SISTA, Katholieke Universiteit Leuven, 3000 Leuven

来源：

Computational Management Science | 2006年 / 3卷 / 2期

关键词：

Fixed-size LS-SVM; Kernel based methods; Least squares support vector machines; Load forecasting; Nyström approximation; Primal space regression; Sparseness; Time series;

D O I：

10.1007/s10287-005-0003-7

中图分类号：

学科分类号：

摘要：

Based on the Nyström approximation and the primal-dual formulation of the least squares support vector machines, 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. The forecasting performance, over ten different load series, shows satisfactory results when the sparse representation is built with less than 3% of the available sample. © Springer 2006.

引用

页码：113 / 129

页数：16

共 33 条

[1]

Bjorkstrom A., Sundberg R., A Generalized view on continuum regression, Scand J Stat, 26, pp. 17-30, (1999)

[2]

Bunn D., Forecasting load and prices in competitive power markets, Proc IEEE, 88, 2, pp. 163-169, (2000)

[3]

Chen B.J., Chang M.W., Lin C.J., Load forecasting using support vector machines: A study on EUNITE competition 2001, (2002)

[4]

Cristianini N., Shawe-Taylor J., An Introduction to Support Vector Machines, (2000)

[5]

Engle R., Granger C.J., Rice J., Weiss A., Semiparametric estimates of the relation between weather and electricity sales, J Am Stat Assoc, 81, 394, pp. 310-320, (1986)

[6]

Espinoza M., Suykens J.A.K., De Moor B., Least squares support vector machines and primal space estimation, Proceedings of the IEEE 42nd Conference on Decision and Control, pp. 3451-3456, (2003)

[7]

Espinoza M., Pelckmans K., Hoegaerts L., Suykens J.A.K., De Moor B., A comparative study of LS-SVM applied to the silverbox Identification problem, Proceedings of the 6th IFAC Conference on Nonlinear Control Systems (NOLCOS), (2004)

[8]

Espinoza M., Joye C., Belmans R., De Moor B., Short term load forecasting, profile identification and customer segmentation: A methodology based on periodic time series, IEEE Trans Power Syst, 20, 3, pp. 1622-1630, (2005)

[9]

Fay D., Ringwood J., Condon M., Kelly M., 24-h electrical load data - A sequential or partitioned time series, Neurocomputing, 55, pp. 469-498, (2003)

[10]

Frank I., Friedman J., A statistical view of some chemometrics regression tools, Technometrics, 35, pp. 109-148, (1993)

← 1 2 3 4 →