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A modeling approach supporting digital twin engineering: Optimizing the energy consumption of air conditioning facilities
Institute of Computer Science, University of Rostock, Rostock, Germany .
Institute of Computer Science, University of Rostock, Rostock, Germany .
Institute of Computer Science, University of Rostock, Rostock, Germany .
Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics. Institute of Computer Science, University of Rostock, Rostock, Germany .ORCID iD: 0000-0002-7431-8412
2023 (English)In: 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C): Conference Proceedings, IEEE Computer Society, 2023, p. 479-483Conference paper, Published paper (Refereed)
Abstract [en]

Digital Twin (DT) is a concept that has become increasingly relevant in recent years. The basic idea is to connect (physical) facilities or devices with digital representations to monitor, manipulate and predict their behavior in real-time. This work presents a modeling approach supporting DT engineering for air conditioning facilities. We describe the architecture of our system for developing digital representations of physically existing facilities in an industrial use case. The paper focuses on (a) the developed modeling tool, (b) the cloud service configuration, and (c) data analysis that enables energy optimizations.

Place, publisher, year, edition, pages
IEEE Computer Society, 2023. p. 479-483
Keywords [en]
Air Conditioning, Atmospheric Modeling, Digital Representation, Reliability Engineering, Data Models, Real Time Systems, Behavioral Sciences, Digital Twin, Model Driven Development, Mod Eling Methodologies, Domain Specific Modeling Language, Object Object Object Object, Object Object, Cloud Computing Object Object Object Object Object Object, Time Series Data, Physical Body Object Object Object Object Object Object, Physical System, Notebook, Domain Experts, Object Object Object Object Object Object, Physical Devices, Need For Solutions, Heat Recovery, Metadata File, Predictive Maintenance, Airflow Direction
National Category
Information Systems
Identifiers
URN: urn:nbn:se:hj:diva-63354DOI: 10.1109/MODELS-C59198.2023.00083ISBN: 979-8-3503-2498-3 (electronic)OAI: oai:DiVA.org:hj-63354DiVA, id: diva2:1828099
Conference
2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), Västerås, Sweden, October 1-6, 2023
Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-01-16Bibliographically approved

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Sandkuhl, Kurt

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