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Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation

2021-12Journal article Open access
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dc.contributor.authorSessa, Valentina
dc.contributor.authorBossy, Mireille
dc.contributor.authorSimoes, Sofia
dc.date.accessioned2022-01-25T12:31:57Z
dc.date.available2022-01-25T12:31:57Z
dc.date.issued2021-12
dc.description.abstractABSTRACT: Analyzing the impact of climate variables into the operational planning processes is essential for the robust implementation of a sustainable power system. This paper deals with the modeling of the run-of-river hydropower production based on climate variables on the European scale. A better understanding of future run-of-river generation patterns has important implications for power systems with increasing shares of solar and wind power. Run-of-river plants are less intermittent than solar or wind but also less dispatchable than dams with storage capacity. However, translating time series of climate data (precipitation and air temperature) into time series of run-of-river-based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. This task is also more complex when performed for a large interconnected area. In this work, a model is built for several European countries by using machine learning techniques. In particular, we compare the accuracy of models based on the Random Forest algorithm and show that a more accurate model is obtained when a finer spatial resolution of climate data is introduced. We then discuss the practical applicability of a machine learning model for the medium term forecasts and show that some very context specific but influential events are hard to capture.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSessa, Valentina... [et.al.] - Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation. In: Clean Technologies, 2021, Vol. 3, pp.858-880pt_PT
dc.identifier.doi10.3390/cleantechnol3040050pt_PT
dc.identifier.eissn2571-8797
dc.identifier.urihttp://hdl.handle.net/10400.9/3722
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationEuropean Research Area for Climate Services
dc.relation.publisherversionhttps://doi.org/10.3390/cleantechnol3040050pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectEnergy modelingpt_PT
dc.subjectMachine learningpt_PT
dc.subjectHydropower generationpt_PT
dc.titleAnalyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleEuropean Research Area for Climate Services
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/690462/EU
oaire.citation.endPage880pt_PT
oaire.citation.startPage858pt_PT
oaire.citation.titleClean Technologiespt_PT
oaire.citation.volume3pt_PT
oaire.fundingStreamH2020
person.familyNameSessa
person.familyNameBossy
person.familyNameSimoes
person.givenNameValentina
person.givenNameMireille
person.givenNameSofia
person.identifier.ciencia-idB013-F0D5-FF8E
person.identifier.orcid0000-0002-0083-2515
person.identifier.orcid0000-0002-6972-9022
person.identifier.orcid0000-0003-4304-1411
person.identifier.ridI-3367-2015
person.identifier.scopus-author-id24472270000
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationb062481c-1db8-43dd-89bc-7a5a2886882e
relation.isAuthorOfPublication93a00caf-b57f-412c-bc81-22ff62db3678
relation.isAuthorOfPublication.latestForDiscoveryf622eeee-7b95-403a-8910-6f805c07c0f3
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relation.isProjectOfPublication.latestForDiscoverycbc1c892-2e44-4bf6-991f-dd736cfc2200

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