Support vector machines in the wind energy framework: a new model for wind energy forecasting

Date

2009

Authors

Pascal, Edurne
Irigoyen, Uxue
Cantero Nouqueret, Elena
Loureiro, Yolanda
Lozano, Sergio
Fernandes Correia, Pedro Miguel
Martí, Ignacio

Director

Publisher

European Wind Energy Association
Acceso cerrado / Sarbide itxia
Contribución a congreso / Biltzarrerako ekarpena

Project identifier

Abstract

In this work we expose a new model for wind energy forecasting based in Support Vector Machines. Support vector machines (SVMs) are a group of supervised learning methods that can be applied to classification or regression. They represent an extension to nonlinear models of the generalized portrait algorithm developed by Vladimir Vapnik. Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers in the context of Vapnik's statistical learning theory. Since then SVMs have been successfully applied to real-world data analysis problems, often providing improved results compared with other techniques. The SVMs operate within the framework of regularization theory by minimizing an empirical risk in a well-posed and consistent way. A clear advantage of the support vector approach is that sparse solutions to classification and regression problems are usually obtained: only a few samples are involved in the determination of the classification or regression functions. This fact facilitates the application of SVMs to problems that involve a large amount of data. Joint to that, the use of kernel functions into their algorithms allows the adaptability to nonlinear problems of SVMs. There are many "traditional" problems in the wind power forecasting systems that affect the accuracy and robustness of the forecasts, as availability of real power measures, the existence of wrong data and the nonlinearity of the relationship between meteorological variables and power. So, these robustness and adaptability of SVMs suggest that these types of learning machines could be a good set of tools to solve many problems in the wind energy context.

Description

Acceso cerrado a este documento. No se encuentra disponible para la consulta pública. Depositado en Academica-e para cumplir con los requisitos de evaluación y acreditación académica del autor/a (sexenios, acreditaciones, etc.).

Keywords

Wind energy forecasting, Support vector machines (SVMs), Robustness and adaptability

Department

Estadística, Informática y Matemáticas / Estatistika, Informatika eta Matematika

Faculty/School

Degree

Doctorate program

item.page.cita

Frías, L., Pascal, E., Irigoyen, U., Cantero, E., Loureiro, Y., Lozano, S., Fernandes, P. M., Martí, I. (2009). Support vector machines in the wind energy framework: a new model for wind energy forecasting. In [EWEC], European Wind Energy Conference & Exhibition 2009 (pp. 3990-3998). European Wind Energy Association.

item.page.rights

© 2009 by the respective author(s), and included in these proceedings by permission to the European Wind Energy Association

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