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

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dc.contributor.authorJanzen, Sabine
dc.contributor.authorGdanitz, Natalie
dc.contributor.authorAbdel Khaliq, Lotfy
dc.contributor.authorMunir, Talha
dc.contributor.authorFranzius, Christoph
dc.contributor.authorMaass, Wolfgang
dc.date.accessioned2022-12-27T19:09:29Z
dc.date.available2022-12-27T19:09:29Z
dc.date.issued2023-01-03
dc.description.abstractPower 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.extent10
dc.identifier.doi10.24251/HICSS.2023.450
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.other453fa5a9-5b5e-4130-81f0-65bdfc6ae45e
dc.identifier.urihttps://hdl.handle.net/10125/103081
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Spaces for Sustainability and Resilience in Manufacturing
dc.subjectenergy-driven crisis
dc.subjectoutages
dc.subjectprocess industry
dc.subjectscenario patterns
dc.subjectscenario planning
dc.titleAnticipating Energy-driven Crises in Process Industry by AI-based Scenario Planning
dc.type.dcmitext
prism.startingpage3673

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Data Spaces for Sustainability and Resilience in Manufacturing