dc.contributor.author | Yousefi, Mojtaba | |
dc.contributor.author | Wang, Jinghao | |
dc.contributor.author | Høivik, Øivind Fandrem | |
dc.contributor.author | Rajasekharan, Jayaprakash | |
dc.contributor.author | Wierling, August Hubert | |
dc.contributor.author | Farahmand, Hossein | |
dc.contributor.author | Arghandeh, Reza | |
dc.date.accessioned | 2023-06-09T10:59:19Z | |
dc.date.available | 2023-06-09T10:59:19Z | |
dc.date.created | 2023-05-04T23:08:17Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | https://hdl.handle.net/11250/3070731 | |
dc.description.abstract | Climate change affects patterns and uncertainties associated with river water regimes, which significantly impact hydropower generation and reservoir storage operation. Hence, reliable and accurate short-term inflow forecasting is vital to face climate effects better and improve hydropower scheduling performance. This paper proposes a Causal Variational Mode Decomposition (CVD) preprocessing framework for the inflow forecasting problem. CVD is a preprocessing feature selection framework that is built upon multiresolution analysis and causal inference. CVD can reduce computation time while increasing forecasting accuracy by down-selecting the most relevant features to the target value (inflow in a specific location). Moreover, the proposed CVD framework is a complementary step to any machine learning-based forecasting method as it is tested with four different forecasting algorithms in this paper. CVD is validated using actual data from a river system downstream of a hydropower reservoir in the southwest of Norway. The experimental results show that CVD-LSTM reduces forecasting error metric by almost 70% compared with a baseline (scenario 1) and reduces by 25% compared to an LSTM for the same composition of input data (scenario 4). | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Nature Research | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Short-term inflow forecasting in a dam-regulated river in Southwest Norway using causal variational mode decomposition | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © The Author(s) 2023 | en_US |
dc.source.volume | 13 | en_US |
dc.source.journal | Scientific Reports | en_US |
dc.source.issue | 1 | en_US |
dc.identifier.doi | 10.1038/s41598-023-34133-8 | |
dc.identifier.cristin | 2145682 | |
dc.relation.project | Norges forskningsråd: 309997 | en_US |
dc.source.articlenumber | 7016 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |