Sparse linear wind farm energy forecast

Authors:
Carlos M. Alaíz;Alberto Torres;José R. Dorronsoro
Affiliations:
Departamento de Ingeniería Informática & Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Madrid, Spain;Departamento de Ingeniería Informática & Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Madrid, Spain;Departamento de Ingeniería Informática & 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 sparse linear regression methods to forecast wind farm energy production using numerical weather prediction (NWP) features over several pressure levels, a problem where pattern dimension can become very large. We shall place sparse regression in the context of proximal optimization, which we shall briefly review, and we shall show how sparse methods outperform other models while at the same time shedding light on the most relevant NWP features and on their predictive structure.