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

Research output: Contribution to conference › Paper › peer-review

Original language	English
Pages	10-16
DOIs	https://doi.org/10.1049/icp.2024.3479
Publication status	Published - 3 Oct 2024
Event	Low-Cost Digital Solutions for Industrial Automation - Institute for Manufacturing, Cambridge, Cambridge, United Kingdom Duration: 1 Oct 2024 → 3 Oct 2024 https://engage-events.ifm.eng.cam.ac.uk/Low-CostDigitalSolutionsforIndustrialAutomation

Conference	Low-Cost Digital Solutions for Industrial Automation
Abbreviated title	LoDiSA 2024
Country/Territory	United Kingdom
City	Cambridge
Period	01.10.2024 → 03.10.2024
Internet address	https://engage-events.ifm.eng.cam.ac.uk/Low-CostDigitalSolutionsforIndustrialAutomation

Access to Document

@conference{26fbe443446e4dd286cb5bad3dd60ed7,

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",

month = oct,

day = "3",

doi = "10.1049/icp.2024.3479",

language = "English",

pages = "10--16",

note = "Low-Cost Digital Solutions for Industrial Automation ; Conference date: 01-10-2024 Through 03-10-2024",

url = "https://engage-events.ifm.eng.cam.ac.uk/Low-CostDigitalSolutionsforIndustrialAutomation",

}

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Enhancing operational efficiency in energy-intensive industries through AI: a case study of electricity demand forecasting in foundries

AU - Straßer, Sonja

AU - Jodlbauer, Herbert

AU - Tripathi, Shailesh

PY - 2024/10/3

Y1 - 2024/10/3

U2 - 10.1049/icp.2024.3479

DO - 10.1049/icp.2024.3479

M3 - Paper

SP - 10

EP - 16

T2 - Low-Cost Digital Solutions for Industrial Automation

Y2 - 1 October 2024 through 3 October 2024

ER -