Título

Wind speed forecasting for wind farms: A method based on support vector regression

Autor

GUILLERMO SANTAMARIA BONFIL

ALBERTO REYES BALLESTEROS

CARLOS GERSHENSON GARCIA

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by-nc/4.0

Materias

INGENIERÍA Y TECNOLOGÍA - (CTI) CIENCIAS TECNOLÓGICAS - (CTI) OTRAS ESPECIALIDADES TECNOLÓGICAS - (CTI) OTRAS - (CTI)

Resumen o descripción

In this paper, a hybrid methodology based on Support Vector Regression for wind speed forecasting is proposed. Using the autoregressive model called Time Delay Coordinates, feature selection is performed by the Phase Space Reconstruction procedure. Then, a Support Vector Regression model is trained using univariate wind speed time series. Parameters of Support Vector Regression are tuned by a genetic algorithm. The proposed method is compared against the persistence model, and autoregressive models (AR, ARMA, and ARIMA) tuned by Akaike's Information Criterion and Ordinary Least Squares method. The stationary transformation of time series is also evaluated for the proposed method. Using historical wind speed data from the Mexican Wind Energy Technology Center (CERTE) located at La Ventosa, Oaxaca, M exico, the accuracy of the proposed forecasting method is evaluated for a whole range of short termforecasting horizons (from 1 to 24 h ahead). Results show that, forecasts made with our method are more accurate for medium (5e23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models.

Fecha de publicación

2015

Tipo de publicación

Artículo

Versión de la publicación

Versión publicada

Recurso de información

http://repositorio.ineel.mx/jspui/handle/123456789/216

Formato

application/pdf

Fuente

ISSN 0020-0190

Idioma

Inglés

Audiencia

Público en general

Repositorio Orígen

Repositorio Institucional de Acceso Abierto de Información Científica, Tecnológica y de Innovación del INEEL

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