Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/5298
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dc.contributor.authorLaura Luciana Cavalcanteen
dc.contributor.authorRicardo Jorge Bessaen
dc.contributor.authorMarisa Mendonça Reisen
dc.contributor.authorBrowell,Jen
dc.date.accessioned2018-01-03T00:54:16Z-
dc.date.available2018-01-03T00:54:16Z-
dc.date.issued2017en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/5298-
dc.identifier.urihttp://dx.doi.org/10.1002/we.2029en
dc.description.abstractThe deployment of smart grids and renewable energy dispatch centers motivates the development of forecasting techniques that take advantage of near real-time measurements collected from geographically distributed sensors. This paper describes a forecasting methodology that explores a set of different sparse structures for the vector autoregression (VAR) model using the least absolute shrinkage and selection operator (LASSO) framework. The alternating direction method of multipliers is applied to fit the different LASSO-VAR variants and create a scalable forecasting method supported by parallel computing and fast convergence, which can be used by system operators and renewable power plant operators. A test case with 66 wind power plants is used to show the improvement in forecasting skill from exploring distributed sparse structures. The proposed solution outperformed the conventional autoregressive and vector autoregressive models, as well as a sparse VAR model from the state of the art. Copyright (c) 2016 John Wiley & Sons, Ltd.en
dc.languageengen
dc.relation6019en
dc.relation4882en
dc.relation6477en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleLASSO vector autoregression structures for very short-term wind power forecastingen
dc.typearticleen
dc.typePublicationen
Appears in Collections:CPES - Articles in International Journals

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