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Super Local Models for Wind Power Detection

EasyChair Preprint no. 6094

12 pagesDate: July 16, 2021


Local models can be a useful, necessary tool when dealing with problems with high variance. In particular, wind power forecasting can be benefited from this approach. In this work, we propose a local regression method that defines a particular model for each point of the test set based on its neighborhood. Applying this approach for wind energy prediction, and especially for linear methods, we achieve accurate models that are not dominated by low wind samples, and that implies an improvement also in computational terms. Moreover it will be shown that using linear models allows interpretability, gaining insight on the tackled problem.

Keyphrases: local models, Regression, wind power forecasting

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {María Barroso and Ángela Fernández},
  title = {Super Local Models for Wind Power Detection},
  howpublished = {EasyChair Preprint no. 6094},

  year = {EasyChair, 2021}}
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