Ordinal and nominal classication of wind speed from synoptic pressure patterns

Abstract

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