Diffusion maps and local models for wind power prediction
EntityUAM. Departamento de Ingeniería Informática
PublisherSpringer Berlin Heidelberg
10.1007/978-3-642-33266-1_70Artificial 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.
ISSN0302-9743 (print); 1611-3349 (online)
ISBN978-3-642-33265-4 (print); 978-3-642-33266-1 (online)
Funded byWith 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 data
SubjectsComputation by Abstract Devices; Pattern Recognition; Image Processing and Computer Vision; Informática
NoteThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33266-1_70
Proceedings of 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012
Rights© Springer-Verlag Berlin Heidelberg 2012
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.
Google Scholar:Fernández Pascual, Ángela - Alaiz Gudín, Carlos María - González Marcos, Ana María - Díaz García, Julia - Dorronsoro Ibero, José Ramón
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