dc.contributor.author | Mishra, Sambeet | |
dc.contributor.author | Bordin, Chiara | |
dc.contributor.author | Würsig, Christoph | |
dc.contributor.author | Palu, Ivo | |
dc.date.accessioned | 2019-09-02T07:52:35Z | |
dc.date.available | 2019-09-02T07:52:35Z | |
dc.date.created | 2019-08-12T16:31:57Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | International Journal of Modeling and Optimization. 2019, 9 (3), 146-149. | nb_NO |
dc.identifier.issn | 2010-3697 | |
dc.identifier.uri | http://hdl.handle.net/11250/2611941 | |
dc.description.abstract | In mathematical optimization uncertainty is expressed through scenarios. auto-regressive integrated moving average (ARIMA) is one of the known practice to generate scenarios. This paper is about scenario generation using multivariate data: electrical power demand, wind power generation and energy market price. An ARIMA model along with Copula is implemented for scenario generation. The results are presented and discussed. | nb_NO |
dc.description.abstract | Multivariate Scenario Generation -An Arima and Copula Approach | nb_NO |
dc.language.iso | eng | nb_NO |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.title | Multivariate Scenario Generation -An Arima and Copula Approach | nb_NO |
dc.type | Journal article | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.source.pagenumber | 146-149 | nb_NO |
dc.source.volume | 9 | nb_NO |
dc.source.journal | International Journal of Modeling and Optimization | nb_NO |
dc.source.issue | 3 | nb_NO |
dc.identifier.doi | 10.7763/IJMO.2019.V9.700 | |
dc.identifier.cristin | 1715374 | |
cristin.unitcode | 7548,50,0,0 | |
cristin.unitname | Energisystemer | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 0 | |