Diffusion maps and local models for wind power prediction
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2012Citation
10.1007/978-3-642-33266-1_70
Artificial Neural Networks and Machine Learning – ICANN 2012: 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part II. Lecture Notes in Computer Science, Volumen 7553. Springer, 2012. 565-572.
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-642-33265-4 (print); 978-3-642-33266-1 (online)DOI
10.1007/978-3-642-33266-1_70Funded by
With partial support from grant TIN2010-21575-C02-01 of Spain’s Ministerio de Economía y Competitividad and the UAM–ADIC Chair for Machine Learning in Modelling and Prediction. The first author is also supported by an FPI-UAM grant and kindly thanks the Applied Mathematics Department of Yale University for receiving her during a visit. The second author is supported by the FPU-MEC grant AP2008-00167. We also thank Red Eléctrica de España, Spain’s TSO, for providing historic wind energy dataEditor's Version
http://dx.doi.org/10.1007/978-3-642-33266-1_70Subjects
Computation by Abstract Devices; Pattern Recognition; Image Processing and Computer Vision; InformáticaNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33266-1_70Proceedings of 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012
Rights
© Springer-Verlag Berlin Heidelberg 2012Abstract
In this work we will apply Diffusion Maps (DM), a recent technique for dimensionality reduction and clustering, to build local models for wind energy forecasting. We will compare ridge regression models for K–means clusters obtained over DM features, against the models obtained for clusters constructed over the original meteorological data or principal components, and also against a global model. We will see that a combination of the DM model for the low wind power region and the global model elsewhere outperforms other options.
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Google Scholar:Fernández Pascual, Ángela
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Alaiz Gudín, Carlos María
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González Marcos, Ana María
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Díaz García, Julia
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Dorronsoro Ibero, José Ramón
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