Title Multiclass Prediction of Wind Power Ramp Events Combining Reservoir Computing and Support Vector Machines
Authors Dorado-Moreno, Manuel , DURAN ROSAL, ANTONIO MANUEL, Guijo-Rubio, David , Antonio Gutierrez, Pedro , Prieto, Luis , Salcedo-Sanz, Sancho , Hervas-Martinez, Cesar
External publication Si
Means Lect. Notes Comput. Sci.
Scope Proceedings Paper
Nature Científica
JCR Quartile 4
SJR Quartile 2
SJR Impact 0.33900
Publication date 01/01/2016
ISI 000387750600028
DOI 10.1007/978-3-319-44636-3_28
Abstract This paper proposes a reservoir computing architecture for predicting wind power ramp events (WPREs), which are strong increases or decreases of wind speed in a short period of time. This is a problem of high interest, because WPREs increases the maintenance costs of wind farms and hinders the energy production. The standard echo state network architecture is modified by replacing the linear regression used to compute the reservoir outputs by a nonlinear support vector machine, and past ramp function values are combined with reanalysis data to perform the prediction. Another novelty of the study is that we will predict three type of events (negative ramps, non-ramps and positive ramps), instead of binary classification of ramps, given that the type of ramp can be crucial for the correct maintenance of the farm. The model proposed obtains satisfying results, being able to correctly predict around 70% of WPREs and outperforming other models.
Keywords Wind ramp events; Reservoir computing; Echo state networks; Support vector machines
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