Current methods and advances in forecasting of wind power generation

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dc.contributor.author Foley, Aoife M.
dc.contributor.author Leahy, Paul G.
dc.contributor.author Marvuglia, Antonino
dc.contributor.author McKeogh, Eamon J.
dc.date.accessioned 2014-12-09T13:14:12Z
dc.date.available 2014-12-09T13:14:12Z
dc.date.issued 2012-01
dc.identifier.citation FOLEY, 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.2011.05.033 en
dc.identifier.volume 37 en
dc.identifier.issued 1 en
dc.identifier.startpage 1 en
dc.identifier.endpage 8 en
dc.identifier.issn 0960-1481
dc.identifier.uri http://hdl.handle.net/10468/1735
dc.identifier.doi 10.1016/j.renene.2011.05.033
dc.description.abstract Wind 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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier en
dc.relation.uri http://www.sciencedirect.com/science/article/pii/S0960148111002850
dc.rights Copyright © 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.2011.05.033 en
dc.subject Meteorology en
dc.subject Numerical weather prediction en
dc.subject Probabilistic forecasting en
dc.subject Wind integration wind power forecasting en
dc.title Current methods and advances in forecasting of wind power generation en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Paul Leahy, Civil Engineering, University College Cork, Cork, Ireland. +353-21-490-3000 Email: paul.leahy@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2014-11-21T16:32:25Z
dc.description.version Accepted Version en
dc.internal.rssid 103616169
dc.description.status Peer reviewed en
dc.identifier.journaltitle Renewable Energy en
dc.internal.copyrightchecked No Renewable Energy is classified as a 'Green' journal re: post-print archiving by romeo.eprints.org http://romeo.eprints.org/publishers/30.html !!CORA!! AV permitted with set statement. en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress paul.leahy@ucc.ie en


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