Diffusion methods for wind power ramp detection
EntityUAM. Departamento de Ingeniería Informática
PublisherSpringer Berlin Heidelberg
10.1007/978-3-642-38679-4_9Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Proceedings, Part I. Lecture Notes in Computer Science, Volumen 7902. Springer, 2013. 106-113.
ISSN0302-9743 (print); 1611-3349 (online)
ISBN978-3-642-38678-7 (print); 978-3-642-38679-4 (online)
Funded byWith partial support from Spain's grant TIN2010-21575- C02-01 and the UAM-ADIC Chair for Machine Learning. The rst author is also supported by an FPI-UAM grant and kindly thanks the Applied Mathematics Department of Yale University for receiving her during her visits. The second author is supported by the FPU-MEC grant AP2008-00167.
SubjectsAnisotropic Diffusion; Diffusion distance; Diffusion Methods; Wind power ramps; Informática
NoteThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38679-4_9
Proceedings of 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Part I
Rights© Springer-Verlag Berlin Heidelberg 2013
The prediction and management of wind power ramps is currently receiving large attention as it is a crucial issue for both system operators and wind farm managers. However, this is still an issue far from being solved and in this work we will address it as a classification problem working with delay vectors of the wind power time series and applying local Mahalanobis K-NN search with metrics derived from Anisotropic Diffusion methods. The resulting procedures clearly outperform a random baseline method and yield good sensitivity but more work is needed to improve on specificity and, hence, precision.
Google Scholar:Fernández Pascual, Ángela - Alaiz Gudín, Carlos María - González, Ana M. - Díaz García, Julia - Dorronsoro Ibero, José Ramón
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