Vis enkel innførsel

dc.contributor.authorHelseth, Arild
dc.contributor.authorSveen, Eivind Bekken
dc.date.accessioned2020-10-22T12:43:13Z
dc.date.available2020-10-22T12:43:13Z
dc.date.created2020-10-16T09:47:57Z
dc.date.issued2020
dc.identifier.citation2020 17th International Conference on the European Energy Market - EEMen_US
dc.identifier.isbn978-1-7281-6919-4
dc.identifier.issn2165-4093
dc.identifier.urihttps://hdl.handle.net/11250/2684532
dc.description.abstractWe present a framework based on machine learning for reducing the problem size of a short-term hydrothermal scheduling optimization model applied for price forecasting. The general idea is to reduce the optimization problem dimensions by finding patterns in input data, and without compromising the solution quality. The framework was tested on a data description of the Northern European power system, demonstrating significant reductions in computation times.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartof2020 17th International Conference on the European Energy Market - EEM
dc.relation.ispartofseriesInternational Conference on the European Energy Market;2020
dc.titleCombining Machine Learning and Optimization for Efficient Price Forecastingen_US
dc.typeChapteren_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holderIEEEen_US
dc.identifier.doi10.1109/EEM49802.2020.9221968
dc.identifier.cristin1840062
dc.relation.projectNorges forskningsråd: 268014en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel