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Wind power forecasting with machine learning: single and combined methods

2022-09Conference object Open access
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dc.contributor.authorRosa, J.
dc.contributor.authorPestana, Rui
dc.contributor.authorLeandro, Carlos
dc.contributor.authorBrás-Geraldes, Carlos
dc.contributor.authorEsteves, João
dc.contributor.authorCarvalho, D.
dc.date.accessioned2023-04-26T10:02:05Z
dc.date.available2023-04-26T10:02:05Z
dc.date.issued2022-09
dc.description.abstractIn Portugal, wind power represents one of the largest renewable sources of energy in the national energy mix. The investment in wind power started several decades ago and is still on the roadmap of political and industrial players. One example is that by 2030 it is estimated that wind power is going to represent up to 35% of renewable energy production in Portugal. With the growth of the installed wind capacity, the development of methods to forecast the amount of energy generated becomes increasingly necessary. Historically, Numerical Weather Prediction (NWP) models were used. However, forecasting accuracy depends on many variables such as on-site conditions, surrounding terrain relief, local meteorology, etc. Thus, it becomes a challenge to obtain improved results using such methods. This article aims to report the development of a machine learning pipeline with the objective of improving the forecasting capability of the NWP’s to obtain an error lower than 10%.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationROSA, J. [et al] – Wind power forecasting with machine learning: single and combined methods. In20th International Conference on Renewable Energies and Power Quality (ICREPQ’22). Vigo, Spain: Renewable Energy and Power Quality Journal, 2022. ISSN 2172-038X. Vol. 20. Pp. 673-678.pt_PT
dc.identifier.doi10.24084/repqj20.397pt_PT
dc.identifier.issn2172-038X
dc.identifier.urihttp://hdl.handle.net/10400.21/15936
dc.language.isoengpt_PT
dc.publisherRenewable Energy and Power Quality Journalpt_PT
dc.relation4200-201955510A-0-0-00 - FCTpt_PT
dc.relation.publisherversionhttps://www.icrepq.com/icrepq22/397-22-rosa.pdfpt_PT
dc.subjectWind power forecastpt_PT
dc.subjectFeature engineeringpt_PT
dc.subjectMachine learningpt_PT
dc.subjectEnsemble modelspt_PT
dc.subjectRecurrent neural networkpt_PT
dc.titleWind power forecasting with machine learning: single and combined methodspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceVigo, Spain - 27th to 29th July 2022pt_PT
oaire.citation.endPage678pt_PT
oaire.citation.startPage673pt_PT
oaire.citation.title20th International Conference on Renewable Energies and Power Quality (ICREPQ’22)pt_PT
oaire.citation.volume20pt_PT
person.familyNameLeandro
person.familyNameBrás-Geraldes
person.givenNameCarlos
person.givenNameCarlos
person.identifier.ciencia-id081A-ABD6-13DD
person.identifier.orcid0000-0001-5966-6729
person.identifier.orcid0000-0002-1551-6531
person.identifier.ridA-4358-2019
person.identifier.scopus-author-id57197972944
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication5c19491f-a524-47f3-8885-f4e1120874e4
relation.isAuthorOfPublication2e79cc97-7263-448f-b230-3bdc4ebefa9b
relation.isAuthorOfPublication.latestForDiscovery2e79cc97-7263-448f-b230-3bdc4ebefa9b

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