Current methods and advances in forecasting of wind power generation

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dc.contributor.authorFoley, Aoife M.
dc.contributor.authorLeahy, Paul G.
dc.contributor.authorMarvuglia, Antonino
dc.contributor.authorMcKeogh, Eamon J.
dc.date.accessioned2014-12-09T13:14:12Z
dc.date.available2014-12-09T13:14:12Z
dc.date.issued2012-01
dc.date.updated2014-11-21T16:32:25Z
dc.description.abstractWind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationFOLEY, A. M., LEAHY, P. G., MARVUGLIA, A. & MCKEOGH, E. J. 2012. Current methods and advances in forecasting of wind power generation. Renewable Energy, 37 (1), 1-8. doi:10.1016/j.renene.2011.05.033en
dc.identifier.doi10.1016/j.renene.2011.05.033
dc.identifier.endpage8en
dc.identifier.issn0960-1481
dc.identifier.issued1en
dc.identifier.journaltitleRenewable Energyen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/1735
dc.identifier.volume37en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urihttp://www.sciencedirect.com/science/article/pii/S0960148111002850
dc.rights© 2011 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved. NOTICE: this is the author’s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy [Volume 37, Issue 1, January 2012, Pages 1–8] http://dx.doi.org/10.1016/j.renene.2011.05.033en
dc.subjectMeteorologyen
dc.subjectNumerical weather predictionen
dc.subjectProbabilistic forecastingen
dc.subjectWind integration wind power forecastingen
dc.titleCurrent methods and advances in forecasting of wind power generationen
dc.typeArticle (peer-reviewed)en
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