Anticipating Energy-driven Crises in Process Industry by AI-based Scenario Planning

dc.contributor.author Janzen, Sabine
dc.contributor.author Gdanitz, Natalie
dc.contributor.author Abdel Khaliq, Lotfy
dc.contributor.author Munir, Talha
dc.contributor.author Franzius, Christoph
dc.contributor.author Maass, Wolfgang
dc.date.accessioned 2022-12-27T19:09:29Z
dc.date.available 2022-12-27T19:09:29Z
dc.date.issued 2023-01-03
dc.description.abstract Power outages and fluctuations represent serious crisis situations in energy-intensive process industry like glass and paper production, where substances such as oil, gas, wood fibers or chemicals are processed. Power disruptions can interrupt chemical reactions and produce tons of waste as well as damage of machine parts. But, despite of the obvious criticality, handling of outages in manufacturing focuses on commissioning of expensive proprietary power plants to protect against power outages and implicit gut feeling in anticipating potential disruptions. With AISOP, we introduce a model for AI-based scenario planning for predicting crisis situations. AISOP uses conceptual, well-defined scenario patterns to capture entities of crisis situations. Data streams are mapped onto these patterns for determining historic crisis scenarios and predicting future crisis scenarios by using inductive knowledge and machine learning. The model was exemplified within a proof of concept for energy-driven disruption prediction. We were able to evaluate the proposed approach by means of a set of data streams on weather and outages in Germany in terms of performance in predicting potential outages for manufacturers of paper industry with promising results.
dc.format.extent 10
dc.identifier.doi 10.24251/HICSS.2023.450
dc.identifier.isbn 978-0-9981331-6-4
dc.identifier.uri https://hdl.handle.net/10125/103081
dc.language.iso eng
dc.relation.ispartof Proceedings of the 56th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Spaces for Sustainability and Resilience in Manufacturing
dc.subject energy-driven crisis
dc.subject outages
dc.subject process industry
dc.subject scenario patterns
dc.subject scenario planning
dc.title Anticipating Energy-driven Crises in Process Industry by AI-based Scenario Planning
dc.type.dcmi text
prism.startingpage 3673
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