Evaluating nominal and ordinal classifiers for wind speed prediction from synoptic pressure patterns

Research areas:

• Methodology - Machine learning

Year:

2011

Type of Publication:

In Proceedings

Keywords:

ordinal classification, ordinal regression, wind speed, pressure patterns, long-term wind speed prediction, wind farms

Authors:

• Gutiérrez, Pedro Antonio
• Salcedo-Sanz, Sancho
• Hervás-Martínez, César
• Carro-Calvo, Leo
• Sánchez-Monedero, Javier
• Prieto, Luis

Book title:

11th International Conference on Intelligent Systems Design andApplications (ISDA 2011)

Pages:

1265-1270

Address:

Cordoba, Spain, Spain

Month:

November

BibTex:

@conference{Gutierrez2011isda, author = "Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Leo Carro-Calvo and Javier S{\'a}nchez-Monedero and Luis Prieto", abstract = "This paper evaluates the performance of different classifiers when predicting wind speed from synoptic pressure patterns. The prediction problem has been formulated as a classification problem, where the different classes are associated to four values in an ordinal scale. The problem is relevant for long term wind speed prediction and also for wind speed reconstruction in areas (mainly wind farms) where there are not direct wind measures available. The results obtained in this paper present the Support Vector Machine as the best tested classifier for this task. In addition, the use of the intrinsic ordering information of the problem is shown to improve classifier performance", address = "Cordoba, Spain, Spain", booktitle = "11th International Conference on Intelligent Systems Design andApplications (ISDA 2011)", keywords = "ordinal classification, ordinal regression, wind speed, pressure patterns, long-term wind speed prediction, wind farms", month = "nov", pages = "1265-1270", title = "{E}valuating nominal and ordinal classifiers for wind speed prediction from synoptic pressure patterns", year = "2011", }

Abstract:

This paper evaluates the performance of different classifiers when predicting wind speed from synoptic pressure patterns. The prediction problem has been formulated as a classification problem, where the different classes are associated to four values in an ordinal scale. The problem is relevant for long term wind speed prediction and also for wind speed reconstruction in areas (mainly wind farms) where there are not direct wind measures available. The results obtained in this paper present the Support Vector Machine as the best tested classifier for this task. In addition, the use of the intrinsic ordering information of the problem is shown to improve classifier performance