Diffusion maps and local models for wind power prediction

Authors:
Ángela Fernández Pascual;Carlos M. Alaíz;Ana Ma González Marcos;Julia Díaz García;José R. Dorronsoro
Affiliations:
Departamento de Ingeniería Informática and Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Madrid, Spain;Departamento de Ingeniería Informática and Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Madrid, Spain;Departamento de Ingeniería Informática and Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Madrid, Spain;Departamento de Ingeniería Informática and Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Madrid, Spain;Departamento de Ingeniería Informática and Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Madrid, Spain
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Year:
2012

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Abstract

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.