dc.creator | Troncoso Lora, Alicia | es |
dc.creator | Salcedo Sanz, S. | es |
dc.creator | Casanova Mateo, C. | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.creator | Prieto, L. | es |
dc.date.accessioned | 2016-07-15T11:23:37Z | |
dc.date.available | 2016-07-15T11:23:37Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Troncoso Lora, A., Salcedo Sanz, S., Casanova Mateo, C., Riquelme Santos, J.C. y Prieto, L. (2015). Local models-based regression trees for very short-term wind speed prediction. Renewable Energy, 81, 589-598. | |
dc.identifier.issn | 0960-1481 | es |
dc.identifier.uri | http://hdl.handle.net/11441/43679 | |
dc.description.abstract | This paper evaluates the performance of different types of Regression Trees (RTs) in a real problem of
very short-term wind speed prediction from measuring data in wind farms. RT is a solidly established
methodology that, contrary to other soft-computing approaches, has been under-explored in problems of
wind speed prediction in wind farms. In this paper we comparatively evaluate eight different types of RTs
algorithms, and we show that they are able obtain excellent results in real problems of very short-term
wind speed prediction, improving existing classical and soft-computing approaches such as multi-linear
regression approaches, different types of neural networks and support vector regression algorithms in
this problem.We also show that RTs have a very small computation time, that allows the retraining of the
algorithms whenever new wind speed data are collected from the measuring towers. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología ECO2010-22065-C03-02 | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN2011-28956-C02 | es |
dc.description.sponsorship | Junta de Andalucía P12-TIC-1728 | es |
dc.description.sponsorship | Universidad Pablo de Olavide APPB813097 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Renewable Energy, 81, 589-598. | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Wind speed prediction | es |
dc.subject | Very short-term forecasting horizon | es |
dc.subject | regression trees | es |
dc.title | Local models-based regression trees for very short-term wind speed prediction | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | 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 | ECO2010-22065-C03-02 | es |
dc.relation.projectID | TIN2011-28956-C02 | es |
dc.relation.projectID | P12-TIC-1728 | es |
dc.relation.projectID | APPB813097 | es |
dc.identifier.doi | http://dx.doi.org/10.1016/j.renene.2015.03.071 | es |
idus.format.extent | 10 | es |
dc.journaltitle | Renewable Energy | es |
dc.publication.volumen | 81 | es |
dc.publication.initialPage | 589 | es |
dc.publication.endPage | 598 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/43679 | |