Multiclass Prediction of Wind Power Ramp Events Combining Reservoir Computing and Support Vector Machines

Research areas:

• Methodology - Machine learning

Year:

2016

Type of Publication:

In Proceedings

Authors:

• Dorado-Moreno, Manuel
• Durán-Rosal, Antonio Manuel
• Guijo-Rubio, David
• Gutiérrez, Pedro Antonio
• Prieto, L.
• Salcedo-Sanz, Sancho
• Hervás-Martínez, César

Volume:

9868

Book title:

Proceedings of the 17th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2016)

Series:

Lecture Notes in Computer Science (LNCS)

Pages:

300-309

Organization:

Salamanca (Spain)

Month:

14th-16th September

ISBN:

978-3-319-44635-6

ISSN:

0302-9743

BibTex:

@conference{Manolo2016CAEPIA, author = "Manuel Dorado-Moreno and Antonio Manuel Dur{\'a}n-Rosal and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and L. Prieto and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", 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.", booktitle = "Proceedings of the 17th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2016)", doi = "10.1007/978-3-319-44636-3_28", isbn = "978-3-319-44635-6", issn = "0302-9743", month = "14th-16th September", organization = "Salamanca (Spain)", pages = "300-309", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science (LNCS)", title = "{M}ulticlass {P}rediction of {W}ind {P}ower {R}amp {E}vents {C}ombining {R}eservoir {C}omputing and {S}upport {V}ector {M}achines", url = "dx.doi.org/10.1007/978-3-319-44636-3_28", volume = "9868", year = "2016", }

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.