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Less information, similar performance : comparing machine learning-based time series of wind power generation to renewables.ninja

2020Journal article Open access
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dc.contributor.authorBaumgartner, Johann
dc.contributor.authorGruber, Katharina
dc.contributor.authorSimoes, Sofia
dc.contributor.authorSaint-Drenan, Yves-Marie
dc.contributor.authorSchmidt, Johannes
dc.date.accessioned2020-06-01T10:20:37Z
dc.date.available2020-06-01T10:20:37Z
dc.date.issued2020
dc.description.abstractABSTRACT: Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve-based models such as Renewables.ninja (RN) have been used for deriving synthetic time series for wind power generation, despite their need for accurate location information and bias correction, as well as their insufficient replication of extreme events and short-term power ramps. In this paper, we assessed how time series generated by machine learning models (MLMs) compare to RN in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. Hence, we applied neural networks to one wind speed input dataset derived from MERRA2 reanalysis with no location information and two with additional location information. The resulting time series and RN time series were compared with actual generation. All MLM time series feature an equal or even better time series quality than RN, depending on the characteristics considered. We conclude that MLM models show a similar performance to RN, even when information on turbine locations and turbine types is unavailable.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBaumgartner, Johann... [et.al.] - Less information, similar performance : comparing machine learning-based time series of wind power generation to renewables.ninja. In: Energies, 2020, Vol. 13(9), article 2277pt_PT
dc.identifier.doi10.3390/en13092277pt_PT
dc.identifier.issn1996-1073
dc.identifier.urihttp://hdl.handle.net/10400.9/3278
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationEuropean Research Area for Climate Services
dc.relationGoing global? Renewable fuel trade and social land-use restrictions in a low-carbon energy system
dc.relation.publisherversionhttps://doi.org/10.3390/en13092277pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectWind energypt_PT
dc.subjectWind power generationpt_PT
dc.subjectSimulationpt_PT
dc.subjectMachine learningpt_PT
dc.subjectNetwork trainingpt_PT
dc.titleLess information, similar performance : comparing machine learning-based time series of wind power generation to renewables.ninjapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleEuropean Research Area for Climate Services
oaire.awardTitleGoing global? Renewable fuel trade and social land-use restrictions in a low-carbon energy system
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/690462/EU
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/758149/EU
oaire.citation.titleEnergiespt_PT
oaire.citation.volume13pt_PT
oaire.fundingStreamH2020
oaire.fundingStreamH2020
person.familyNameSimoes
person.familyNameSaint-Drenan
person.givenNameSofia
person.givenNameYves-Marie
person.identifier.ciencia-idB013-F0D5-FF8E
person.identifier.orcid0000-0003-4304-1411
person.identifier.orcid0000-0003-1471-1092
person.identifier.ridI-3367-2015
person.identifier.scopus-author-id24472270000
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
project.funder.nameEuropean Commission
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication93a00caf-b57f-412c-bc81-22ff62db3678
relation.isAuthorOfPublication5cf0e15c-26cb-474a-bb7a-65fbe05dc5dc
relation.isAuthorOfPublication.latestForDiscovery93a00caf-b57f-412c-bc81-22ff62db3678
relation.isProjectOfPublicationcbc1c892-2e44-4bf6-991f-dd736cfc2200
relation.isProjectOfPublication61230267-0aae-44bd-997a-a9e66edc05da
relation.isProjectOfPublication.latestForDiscoverycbc1c892-2e44-4bf6-991f-dd736cfc2200

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