@article{EAAI2012,
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 = "Wind speed reconstruction is a challenging problem in areas (mainly wind farms) where there are not direct wind measures available. Different approaches have been applied to this reconstruction, such as measure-correlate-predict 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 classification task, given that the problem is simplified. 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 classification problem, with four wind level categories: low, moderate, high or very high. Moreover, taking into account that these four different 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 classifiers 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 classifier for this task. In addition, the use of the intrinsic ordering information of the problem is shown to significantly improve ranks with respect to nominal classification, although differences in accuracy are small.",
awards = "JCR (2013): 1.962 (category ENGINEERING, MULTIDISCIPLINARY, position 15/87, Q1)",
comments = "JCR (2013): 1.962 (category ENGINEERING, MULTIDISCIPLINARY, position 15/87, Q1)",
doi = "10.1016/j.engappai.2012.10.018",
issn = "0952-1976",
journal = "Engineering Applications of Artificial Intelligence",
keywords = "Ordinal classification, ordinal regression, wind speed, pressure patterns, long-term wind speed prediction, wind farms",
note = "JCR (2013): 1.962 (category ENGINEERING, MULTIDISCIPLINARY, position 15/87, Q1)",
number = "3",
pages = "1008--1015",
title = "{O}rdinal and nominal classification of wind speed from synoptic pressure patterns",
url = "http://dx.doi.org/10.1016/j.engappai.2012.10.018",
volume = "26",
year = "2013",
}