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dc.contributor.authorLarsen, Camilla Thorrud
dc.contributor.authorDoorman, Gerard L.
dc.contributor.authorMo, Birger
dc.date.accessioned2016-02-04T10:16:46Z
dc.date.accessioned2016-03-07T14:05:36Z
dc.date.available2016-02-04T10:16:46Z
dc.date.available2016-03-07T14:05:36Z
dc.date.issued2016
dc.identifier.citationEnergy Procedia 2016, 87:189-196nb_NO
dc.identifier.issn1876-6102
dc.identifier.urihttp://hdl.handle.net/11250/2381688
dc.description.abstractThis paper concerns the joint modelling of wind power and hydro inflow for long-term power system scheduling. We propose a vector autoregressive model applied to deseasonalized series to describe the joint generating mechanism of wind and inflow. The model was applied to daily and weekly bivariate time series comprising wind and inflow from seven regions in Norway. We found evidence of both lagged and contemporaneous dependencies between wind and inflow, in particular, our results indicate that wind is useful in forecasting inflow, but not the other way around. The forecasting performance of the proposed VAR models was compared to that of independent AR models, as well as the persistence forecasts. Our results show that the VAR model was able to provide better forecasts than the AR models and the persistence forecast, for both the daily and weekly time series.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.titleJoint Modelling of Wind Power and Hydro Inflow for Power System Schedulingnb_NO
dc.typePeer reviewednb_NO
dc.typeJournal articleen_GB
dc.date.updated2016-02-04T10:16:46Z
dc.source.volume87nb_NO
dc.source.journalEnergy Procedianb_NO
dc.identifier.doi10.1016/j.egypro.2015.12.350
dc.identifier.cristin1331898
dc.description.localcode© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.nb_NO


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