Abstract
Wind speed reconstruction is a challenging problem in areas (mainly wind farms) where there are very few direct wind measurements available. In this paper we discuss a methodology for wind speed reconstruction in wind farms from pressure measurements. Specifically, we tackle the problem of wind speed prediction from synoptic pressure patterns by considering a regression problem between a grid of sea-level pressure values and a wind speed module measure. The performance of a Support Vector Regression algorithm is analyze, together with the inclusion of a genetic algorithm in order to perform a feature selection for the input variables (pressure). Results considering real wind speed data in Boston, USA, with sea-level pressure values from two alternative global meteorological models (NOAA and ECMWF) are presented.
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References
Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., Feitosa, E.: A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews 12, 1725–1744 (2008)
Carro-Calvo, L., Salcedo-Sanz, S., Kirchner-Bossi, N., Portilla-Figueras, A., Prietoc, L., Garcia-Herrera, R., Hernández-Martín, E.: Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing. Energy 36, 1571–1581 (2011)
Smola, A.J., Murata, N., Schölkopf, B., Muller, K.: Asymptotically optimal choice of ε-loss for support vector machines. In: Proc. of the 8th International Conference on Artificial Neural Networks, Perspectives in Neural Computing (1998)
Mohandes, M.A., Halawani, T.O., Rehman, S., Hussain, A.A.: Support vector machines for wind speed prediction. Renewable Energy 29(6), 939–947 (2004)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing (1998)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
http://www.esrl.noaa.gov/psd/data/reanalysis/reanalysis.shtml
Dee, D.P., Uppal, S.M., Simmons, A.J., et al.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society 137(656), 553–597 (2011)
Eiben, A.E., Smith, J.E.: Introduction to evolutionary computing, 1st edn. Natural Computing Series. Springer (2003)
Zhang, W.G., Goh, A.: Multivariate adaptive regression splines for analysis of geotechnical engineering systems. Computers and Geotechnics 48, 82–95 (2013)
Jekabsons, G.: ARESLab: Adaptive Regression Splines toolbox for Matlab (2011), http://www.cs.rtu.lv/jekabsons/
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Saavedra-Moreno, B., Salcedo-Sanz, S., Carro-Calvo, L., Portilla-Figueras, A., Magdalena-Saiz, J. (2013). Reconstruction of Wind Speed Based on Synoptic Pressure Values and Support Vector Regression. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_38
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DOI: https://doi.org/10.1007/978-3-642-41278-3_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41277-6
Online ISBN: 978-3-642-41278-3
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