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

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.