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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Arpogaus, Marcel | - |
dc.contributor.author | Voss, Marcus | - |
dc.contributor.author | Sick, Beate | - |
dc.contributor.author | Nigge-Uricher, Mark | - |
dc.contributor.author | Dürr, Oliver | - |
dc.date.accessioned | 2024-03-01T09:48:38Z | - |
dc.date.available | 2024-03-01T09:48:38Z | - |
dc.date.issued | 2023-06-15 | - |
dc.identifier.issn | 1949-3053 | de_CH |
dc.identifier.issn | 1949-3061 | de_CH |
dc.identifier.uri | https://doi.org/10.48550/arXiv.2204.13939 | de_CH |
dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/30097 | - |
dc.description.abstract | The 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.iso | en | de_CH |
dc.publisher | IEEE | de_CH |
dc.relation.ispartof | IEEE Transactions on Smart Grid | de_CH |
dc.rights | Licence according to publishing contract | de_CH |
dc.subject | Computer Science | de_CH |
dc.subject | Statistics | de_CH |
dc.subject | Machine learning | de_CH |
dc.subject | Methodology | de_CH |
dc.subject | Application | de_CH |
dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
dc.subject.ddc | 510: Mathematik | de_CH |
dc.title | Short-term density forecasting of low-voltage load using bernstein-polynomial normalizing flows | de_CH |
dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
dcterms.type | Text | de_CH |
zhaw.departement | School of Engineering | de_CH |
zhaw.organisationalunit | Institut für Datenanalyse und Prozessdesign (IDP) | de_CH |
dc.identifier.doi | 10.1109/TSG.2023.3254890 | de_CH |
zhaw.funding.eu | No | de_CH |
zhaw.issue | 6 | de_CH |
zhaw.originated.zhaw | Yes | de_CH |
zhaw.pages.end | 4911 | de_CH |
zhaw.pages.start | 4902 | de_CH |
zhaw.publication.status | publishedVersion | de_CH |
zhaw.volume | 14 | de_CH |
zhaw.publication.review | Peer review (Publikation) | de_CH |
zhaw.author.additional | No | de_CH |
zhaw.display.portrait | Yes | de_CH |
Appears in collections: | Publikationen School of Engineering |
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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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