Ordinal and nominal classication of wind speed from synoptic pressure patterns
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Author
Gutiérrez, P.A.
Salcedo Sanz, S.
Hervás-Martínez, César
Carro-Calvo, L.
Sánchez-Monedero, J.
Prieto, L.
Date
2017-01-18Subject
Ordinal ClassificationOrdinal regression
Wind speed
Pressure patterns
Long-term wind speed prediction
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Show full item recordAbstract
Wind speed reconstruction is a challenging problem in areas (mainly wind
farms) where there are not direct wind measures available. Di erent approaches
have been applied to this reconstruction, such as measure-correlatepredict
algorithms, approaches based on physical models such as reanalysis
methods, or more recently, indirect measures such as pressure, and its relation
to wind speed. This paper adopts the latter method, and deals with wind
speed estimation in wind farms from pressure measures, but including different
novelties in the problem treatment. Existing synoptic pressure-based
indirect approaches for wind speed estimation are based on considering the
wind speed as a continuous target variable, estimating then the corresponding
wind series of continuous values. However, the exact wind speed is not
always needed by wind farms managers, and a general idea of the level of
speed is, in the majority of cases, enough to set functional operations for the
farm (such as wind turbines stop, for example). Moreover, the accuracy of the models obtained is usually improved for the classi cation task, given that
the problem is simpli ed. Thus, this paper tackles the problem of wind speed
prediction from synoptic pressure patterns by considering wind speed as a
discrete variable and, consequently, wind speed prediction as a classi cation
problem, with four wind level categories: low, moderate, high or very high.
Moreover, taking into account that these four di erent classes are associated
to four values in an ordinal scale, the problem can be considered as an ordinal
regression problem. The performance of several ordinal and nominal classi-
ers and the improvement achieved by considering the ordering information
are evaluated. The results obtained in this paper present the Support Vector
Machine as the best tested classi er for this task. In addition, the use of
the intrinsic ordering information of the problem is shown to signi cantly
improve ranks with respect to nominal classi cation, although di erences in
accuracy are small