Performance comparison of multilayer perceptrons and support vector machines in a short-term wind speed prediction problem

Title:

Performance comparison of multilayer perceptrons and support vector machines in a short-term wind speed prediction problem

Creator:

Salcedo Sanz , S., Ortiz-García, E. G., Pérez-Bellido, Á. M., Portilla-Figueras, A., Prieto, L., Paredes, D., and Correoso, F.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:cb795efe-b11b-4a65-a939-e083e80ce724
uuid:cb795efe-b11b-4a65-a939-e083e80ce724

Subject:

Wind speed forecast, global forecasting models, down-scaling processes, Multilayer Perceptrons, and Support Vector Machines

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper we present a comparison between the performance of Multilayer Perceptrons (MLPs) and Support Vector Machines (SVMs) in a problem of wind speed prediction. Specifically, we analyze the behavior of both algorithms within a larger system of wind speed prediction, formed by global and mesoscale weather forecasting models, and with a final statistical down-scaling process where the MLPs and the SVM are used. The final objective is to forecast the mean hourly wind speed prediction at wind turbines in a wind farm. This is an important parameter used to predict the total power production of the wind farm. The specific model for the short-term wind speed forecast we use integrates two different meteorological prediction global models, observations at the surface level and in different heights using atmospheric soundings. Also, it includes a mesoscale prediction model producing the inputs used in the MLP or the SVM, which will forecast the final wind speed at each turbine of the wind farm. In the experiments carried out we compare the results obtained using the MLP or SVM as final steps of the prediction system. Interesting differences of performance can be found when using MLPs or SVMs, which we analyze in this paper. The results obtained are encouraging anyway, and good short-term predictions of wind speed at specific points are obtained with both techniques.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2009 Volume:19 | Number:1

Harvested from:

CDK

Metadata only:

false