Enhancing operational efficiency in energy-intensive industries through AI: a case study of electricity demand forecasting in foundries

Publikation: Konferenzbeitrag › Papier › Begutachtung

Originalsprache	Englisch
Seiten	10-16
DOIs	https://doi.org/10.1049/icp.2024.3479
Publikationsstatus	Veröffentlicht - 3 Okt. 2024
Veranstaltung	Low-Cost Digital Solutions for Industrial Automation - Institute for Manufacturing, Cambridge, Cambridge, Großbritannien/Vereinigtes Königreich Dauer: 1 Okt. 2024 → 3 Okt. 2024 https://engage-events.ifm.eng.cam.ac.uk/Low-CostDigitalSolutionsforIndustrialAutomation

Konferenz	Low-Cost Digital Solutions for Industrial Automation
Kurztitel	LoDiSA 2024
Land/Gebiet	Großbritannien/Vereinigtes Königreich
Ort	Cambridge
Zeitraum	01.10.2024 → 03.10.2024
Internetadresse	https://engage-events.ifm.eng.cam.ac.uk/Low-CostDigitalSolutionsforIndustrialAutomation

Zugriff auf Dokument

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title = "Enhancing operational efficiency in energy-intensive industries through AI: a case study of electricity demand forecasting in foundries",

author = "Sonja Stra{\ss}er and Herbert Jodlbauer and Shailesh Tripathi",

year = "2024",

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doi = "10.1049/icp.2024.3479",

language = "English",

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Publikation: Konferenzbeitrag › Papier › Begutachtung

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AU - Straßer, Sonja

AU - Jodlbauer, Herbert

AU - Tripathi, Shailesh

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