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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorArpogaus, Marcel-
dc.contributor.authorVoss, Marcus-
dc.contributor.authorSick, Beate-
dc.contributor.authorNigge-Uricher, Mark-
dc.contributor.authorDürr, Oliver-
dc.date.accessioned2024-03-01T09:48:38Z-
dc.date.available2024-03-01T09:48:38Z-
dc.date.issued2023-06-15-
dc.identifier.issn1949-3053de_CH
dc.identifier.issn1949-3061de_CH
dc.identifier.urihttps://doi.org/10.48550/arXiv.2204.13939de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30097-
dc.description.abstractThe transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. Probabilistic load forecasts take uncertainties into account and thus allow more informed decision-making for the planning and operation of low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein polynomial normalizing flows, where a neural network controls the parameters of the flow. In an empirical study with 3639 smart meter customers, our density predictions for 24h-ahead load forecasting compare favorably against Gaussian and Gaussian mixture densities. Furthermore, they outperform a non-parametric approach based on the pinball loss, especially in low-data scenarios.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Transactions on Smart Gridde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectComputer Sciencede_CH
dc.subjectStatisticsde_CH
dc.subjectMachine learningde_CH
dc.subjectMethodologyde_CH
dc.subjectApplicationde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleShort-term density forecasting of low-voltage load using bernstein-polynomial normalizing flowsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1109/TSG.2023.3254890de_CH
zhaw.funding.euNode_CH
zhaw.issue6de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end4911de_CH
zhaw.pages.start4902de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume14de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Arpogaus, M., Voss, M., Sick, B., Nigge-Uricher, M., & Dürr, O. (2023). Short-term density forecasting of low-voltage load using bernstein-polynomial normalizing flows. IEEE Transactions on Smart Grid, 14(6), 4902–4911. https://doi.org/10.1109/TSG.2023.3254890
Arpogaus, M. et al. (2023) ‘Short-term density forecasting of low-voltage load using bernstein-polynomial normalizing flows’, IEEE Transactions on Smart Grid, 14(6), pp. 4902–4911. Available at: https://doi.org/10.1109/TSG.2023.3254890.
M. Arpogaus, M. Voss, B. Sick, M. Nigge-Uricher, and O. Dürr, “Short-term density forecasting of low-voltage load using bernstein-polynomial normalizing flows,” IEEE Transactions on Smart Grid, vol. 14, no. 6, pp. 4902–4911, Jun. 2023, doi: 10.1109/TSG.2023.3254890.
ARPOGAUS, Marcel, Marcus VOSS, Beate SICK, Mark NIGGE-URICHER und Oliver DÜRR, 2023. Short-term density forecasting of low-voltage load using bernstein-polynomial normalizing flows. IEEE Transactions on Smart Grid [online]. 15 Juni 2023. Bd. 14, Nr. 6, S. 4902–4911. DOI 10.1109/TSG.2023.3254890. Verfügbar unter: https://doi.org/10.48550/arXiv.2204.13939
Arpogaus, Marcel, Marcus Voss, Beate Sick, Mark Nigge-Uricher, and Oliver Dürr. 2023. “Short-Term Density Forecasting of Low-Voltage Load Using Bernstein-Polynomial Normalizing Flows.” IEEE Transactions on Smart Grid 14 (6): 4902–11. https://doi.org/10.1109/TSG.2023.3254890.
Arpogaus, Marcel, et al. “Short-Term Density Forecasting of Low-Voltage Load Using Bernstein-Polynomial Normalizing Flows.” IEEE Transactions on Smart Grid, vol. 14, no. 6, June 2023, pp. 4902–11, https://doi.org/10.1109/TSG.2023.3254890.


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