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Article: Short-term nodal voltage forecasting for power distribution grids: An ensemble learning approach

TitleShort-term nodal voltage forecasting for power distribution grids: An ensemble learning approach
Authors
KeywordsNodal voltage forecasting
Ensemble learning
Quantile regression averaging
Distribution grids
Situation awareness
Issue Date2021
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/apenergy
Citation
Applied Energy, 2021, v. 304, article no. 117880 How to Cite?
AbstractThe integration of distributed energy resources (DER) complicates the operation of the power distribution grids, and the nodal voltage may violate frequently. Making accurate predictions of the nodal voltage is fundamental for voltage regulation of the distribution grid. Even though energy forecasting has been widely studied, voltage is still a rarely touched area. This paper enriches the research by proposing an ensemble approach for both deterministic and probabilistic short-term nodal voltage forecasting. Specifically, a new joint model- and data-driven feature selection is first performed to select the most relevant features for distribution grid voltage forecasting. Then, different individual forecasting models are trained using the selected features. On this basis, simple weighted averaging and quantile regression averaging approaches are applied to combine the individual models for deterministic and probabilistic forecasting, respectively. Finally, case studies are conducted on a real-world distribution grid to verify the effectiveness and superiority of the proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/304676
ISSN
2023 Impact Factor: 10.1
2023 SCImago Journal Rankings: 2.820
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Y-
dc.contributor.authorVon Krannichfeldt, L-
dc.contributor.authorZufferey, T-
dc.contributor.authorToubeau, J-
dc.date.accessioned2021-10-05T02:33:33Z-
dc.date.available2021-10-05T02:33:33Z-
dc.date.issued2021-
dc.identifier.citationApplied Energy, 2021, v. 304, article no. 117880-
dc.identifier.issn0306-2619-
dc.identifier.urihttp://hdl.handle.net/10722/304676-
dc.description.abstractThe integration of distributed energy resources (DER) complicates the operation of the power distribution grids, and the nodal voltage may violate frequently. Making accurate predictions of the nodal voltage is fundamental for voltage regulation of the distribution grid. Even though energy forecasting has been widely studied, voltage is still a rarely touched area. This paper enriches the research by proposing an ensemble approach for both deterministic and probabilistic short-term nodal voltage forecasting. Specifically, a new joint model- and data-driven feature selection is first performed to select the most relevant features for distribution grid voltage forecasting. Then, different individual forecasting models are trained using the selected features. On this basis, simple weighted averaging and quantile regression averaging approaches are applied to combine the individual models for deterministic and probabilistic forecasting, respectively. Finally, case studies are conducted on a real-world distribution grid to verify the effectiveness and superiority of the proposed method.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/apenergy-
dc.relation.ispartofApplied Energy-
dc.subjectNodal voltage forecasting-
dc.subjectEnsemble learning-
dc.subjectQuantile regression averaging-
dc.subjectDistribution grids-
dc.subjectSituation awareness-
dc.titleShort-term nodal voltage forecasting for power distribution grids: An ensemble learning approach-
dc.typeArticle-
dc.identifier.emailWang, Y: yiwang@eee.hku.hk-
dc.identifier.authorityWang, Y=rp02900-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.apenergy.2021.117880-
dc.identifier.scopuseid_2-s2.0-85115350572-
dc.identifier.hkuros326115-
dc.identifier.volume304-
dc.identifier.spagearticle no. 117880-
dc.identifier.epagearticle no. 117880-
dc.identifier.isiWOS:000701881800007-
dc.publisher.placeUnited Kingdom-

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