dc.creator | Cabello López, Tomás | es |
dc.creator | Cañizares Juan, Manuel | es |
dc.creator | Carranza García, Manuel | es |
dc.creator | García Gutiérrez, Jorge | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2022-12-12T12:36:42Z | |
dc.date.available | 2022-12-12T12:36:42Z | |
dc.date.issued | 2022 | |
dc.identifier.isbn | 978-3-031-15470-6 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/140330 | |
dc.description.abstract | Most of the current data sources generate large amounts of
data over time. Renewable energy generation is one example of such data
sources. Machine learning is often applied to forecast time series. Since
data flows are usually large, trends in data may change and learned pat terns might not be optimal in the most recent data. In this paper, we
analyse wind energy generation data extracted from the Sistema de Infor mación del Operador del Sistema (ESIOS) of the Spanish power grid. We
perform a study to evaluate detecting concept drifts to retrain models
and thus improve the quality of forecasting. To this end, we compare the
performance of a linear regression model when it is retrained randomly
and when a concept drift is detected, respectively. Our experiments show
that a concept drift approach improves forecasting between a 7.88% and
a 33.97% depending on the concept drift technique applied | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación PID2020-117954RB-C22 | es |
dc.description.sponsorship | Junta de Andalucía US-1263341 | es |
dc.description.sponsorship | Junta de Andalucía P18-RT-2778 | es |
dc.format | application/pdf | es |
dc.format.extent | 8 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Machine Learning | es |
dc.subject | Concept drift detection | es |
dc.subject | Data streaming | es |
dc.subject | Time series | es |
dc.subject | Wind energy forecasting | es |
dc.title | Concept Drift Detection to Improve Time Series Forecasting of Wind Energy Generation | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | PID2020-117954RB-C22 | es |
dc.relation.projectID | US-1263341 | es |
dc.relation.projectID | P18-RT-2778 | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-031-15471-3_12 | es |
dc.identifier.doi | 10.1007/978-3-031-15471-3_12 | es |
dc.contributor.group | Universidad de Sevilla. TIC-134: Sistemas Informáticos | es |
dc.publication.initialPage | 133 | es |
dc.publication.endPage | 140 | es |
dc.eventtitle | HAIS 2022: 17th International Conference on Hybrid Artificial Intelligence Systems | es |
dc.eventinstitution | Salamanca, España | es |
dc.relation.publicationplace | Cham, Switzerland | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Junta de Andalucía | es |