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Cited by 33Digital Transformation in IndustryIntegrated Energy Systems OptimizationPower Systems and Technologies

Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

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Kasper, Lukas, Birkelbach, Felix, Schwarzmayr, Paul, Steindl, Gernot, Ramsauer, Daniel, Hofmann, René·2022·Applied Sciences
DOI 10.3390/app12146981
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Abstract

Digitalization and concepts such as digital twins (DT) are expected to have huge potential to improve efficiency in industry, in particular, in the energy sector. Although the number and maturity of DT concepts is increasing, there is still no standardized framework available for the implementation of DTs for industrial energy systems (IES). On the one hand, most proposals focus on the conceptual side of components and leave most implementation details unaddressed. Specific implementations, on the other hand, rarely follow recognized reference architectures and standards. Furthermore, most related work on DTs is done in manufacturing, which differs from DTs in energy systems in various aspects, regarding, for example, multiple time-scales, strong nonlinearities and uncertainties. In the present work, we identify the most important requirements for DTs of IES. We propose a DT platform based on the five-dimensional DT modeling concept with a low level of abstraction that is tailored to the identified requirements. We address current technical implementation barriers and provide practical solutions for them. Our work should pave the way to standardized DT platforms and the efficient encapsulation of DT service engineering by domain experts. Thus, DTs could be easy to implement in various IES-related use cases, host any desired models and services, and help get the most out of the individual applications. This ultimately helps bridge the interdisciplinary gap between the latest research on DTs in the domain of computer science and industrial automation and the actual implementation and value creation in the traditional energy sector.

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  • Thanks to digitalization, certain tasks can be carried out by machines to reduce labor time.

    “Elimination of tedious manual processes Thanks to digitalization, certain tasks can be carried out by machines in order to reduce labor time [40].”

    From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [40]

    The passage following Section 2.2 says digital applications can automate processes and improve their efficiency in industrial energy systems. It does not specifically say that machines perform tedious manual tasks or that automation reduces labor time. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Digital Twins increase the accuracy of prognoses and diagnoses through accurate analysis of big data.

    “Increased accuracy of its prognoses and diagnoses Accurate analysis of big data [40].”

    From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [40]

    Section 2.2 says machine-learning applications can improve the accuracy of demand, generation and price forecasts. The discussion of digital-twin applications describes fault classification and predictive maintenance, but does not say that digital twins improve the accuracy of diagnoses or that accurate big-data analysis is the mechanism. No page number is available in our copy. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Digital Twins improve decision-making, efficiency, cost savings, increased security, sustainability, and better visibility.

    “Improved decision-making Improved efficiency, cost savings, increased security, sustainability, and better visibility [40].”

    From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [40]

    Section 2.2 describes potential gains in efficiency, cost reduction, environmental protection and transparency from digital applications, and Section 4.2 says a digital-twin platform can integrate those applications. The paper does not establish the entire list as achieved benefits: it treats increased security as an ideal requirement, and the passages found do not clearly establish improved decision-making as a benefit of the platform. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The benefits of Digital Twins include improved decision-making, increased operational efficiency, and enhanced risk assessment.

    “As shown in Table 3, the benefits of Digital Twins include improved decision-making, increased operational efficiency, and enhanced risk assessment [40].”

    From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [40]

    The Introduction says digital-twin services can support complex decision-making, and the discussion of digital applications describes improving process efficiency and optimizing operations. The paper discusses monitoring, fault detection, and safety, but the passages do not establish enhanced risk assessment as a benefit. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 88

  1. Gibb, D.; Johnson, M.; Romaní, J.; Gasia, J.; Cabeza, L.F.; Seitz, A. Process integration of thermal energy storage systems- Evaluation methodology and case studies. Appl. Energy 2018, 230, 750-760. [CrossRef] · DOI
  2. Commission, E.; Centre, J.R.; Kavvadias, K.; Jiménez Navarro, J.; Thomassen, G. Decarbonising the EU Heating Sector: Integration of the Power and Heating Sector; Publications Office: Luxembourg, 2019. [CrossRef] · DOI
  3. Mendes, G.; Ioakimidis, C.; Ferrão, P. On the planning and analysis of Integrated Community Energy Systems: A review and survey of available tools. Renew. Sustain. Energy Rev. 2011, 15, 4836-4854. . [CrossRef] · DOI
  4. Parida, V.; Sjödin, D.; Reim, W. Reviewing literature on digitalization, business model innovation, and sustainable industry: Past achievements and future promises. Sustainability 2019, 11, 391. [CrossRef] · DOI
  5. Lange, S.; Pohl, J.; Santarius, T. Digitalization and energy consumption. Does ICT reduce energy demand? Ecol. Econ. 2020, 176, 106760. [CrossRef] · DOI
  6. Kiel, D.; Müller, J.; Arnold, C.; Voigt, K.I. Sustainable Value Creation: Benefits and Challenges of Industry 4.0. Int. J. Innov. Manag. 2017, 21, 1740015. [CrossRef] · DOI
  7. Lu, Y.; Liu, C.; Wang, K.I.K.; Huang, H.; Xu, X. Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robot. Comput.-Integr. Manuf. 2020, 61, 101837. [CrossRef] · DOI
  8. Yu, W.; Patros, P.; Young, B.; Klinac, E.; Walmsley, T.G. Energy digital twin technology for industrial energy management: Classification, challenges and future. Renew. Sustain. Energy Rev. 2022, 161, 112407. [CrossRef] · DOI
  9. Jones, D.; Snider, C.; Nassehi, A.; Yon, J.; Hicks, B. Characterising the Digital Twin: A systematic literature review. CIRP J. Manuf. Sci. Technol. 2020, 29, 36-52. [CrossRef] · DOI · Paper page
  10. Melesse, T.Y.; Pasquale, V.D.; Riemma, S. Digital Twin Models in Industrial Operations: A Systematic Literature Review. Proced. Manuf. 2020, 42, 267-272. [CrossRef] · DOI
  11. Grieves, M. Digital Twin: Manufacturing Excellence through Virtual Factory Replication; White Paper; Florida Institute of Technology: Melbourne, FL, USA, 2014; pp. 1-7.
  12. Negri, E.; Fumagalli, L.; Macchi, M. A Review of the Roles of Digital Twin in CPS-based Production Systems. Proced. Manuf. 2017, 11, 939-948. [CrossRef] · DOI · Paper page
  13. Liu, M.; Fang, S.; Dong, H.; Xu, C. Review of digital twin about concepts, technologies, and industrial applications. J. Manuf. Syst. 2021, 58, 346-361. [CrossRef] · DOI
  14. Wagner, C.; Grothoff, J.; Epple, U.; Drath, R.; Malakuti, S.; Grüner, S.; Hoffmeister, M.; Zimermann, P. The role of the Industry 4.0 asset administration shell and the digital twin during the life cycle of a plant. In Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Torino, Italy, 4-7 September 2018; pp. 1-8. [CrossRef] · DOI
  15. Josifovska, K.; Yigitbas, E.; Engels, G. Reference Framework for Digital Twins within Cyber-Physical Systems. In Proceedings of the 2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS), Montreal, QC, Canada, 28 May 2019; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2019; pp. 25-31. [CrossRef] · DOI
  16. Steindl, G.; Kastner, W. Semantic Microservice Framework for Digital Twins. Appl. Sci. 2021, 11, 5633. [CrossRef] · DOI
  17. Tao, F.; Zhang, M.; Nee, A.Y.C. Digital Twin Driven Smart Manufacturing; Academic Press: Cambridge, MA, USA, 2019. [CrossRef] · DOI
  18. Wanasinghe, T.R.; Wroblewski, L.; Petersen, B.K.; Gosine, R.G.; James, L.A.; De Silva, O.; Mann, G.K.I.; Warrian, P.J. Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges. IEEE Access 2020, 8, 104175-104197. [CrossRef] · DOI
  19. Bruynseels, K.; Santoni de Sio, F.; van den Hoven, J. Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm. Front. Genet. 2018, 9, 31. [CrossRef] · DOI · Paper page
  20. He, R.; Chen, G.; Dong, C.; Sun, S.; Shen, X. Data-driven digital twin technology for optimized control in process systems. ISA Trans. 2019, 95, 221-234. [CrossRef] · DOI
  21. Bottani, E.; Vignali, G.; Carlo Tancredi, G.P. A digital twin model of a pasteurization system for food beverages: tools and architecture. In Proceedings of the 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Cardiff, UK, 15-17 June 2020; pp. 1-8. [CrossRef] · DOI
  22. Brosinsky, C.; Westermann, D.; Krebs, R. Recent and prospective developments in power system control centers: Adapting the digital twin technology for application in power system control centers. In Proceedings of the 2018 IEEE International Energy Conference (ENERGYCON), Limassol, Cyprus, 3-7 June 2018; pp. 1-6. [CrossRef] · DOI · Paper page
  23. O'Dwyer, E.; Pan, I.; Charlesworth, R.; Butler, S.; Shah, N. Integration of an energy management tool and digital twin for coordination and control of multi-vector smart energy systems. Sustain. Cities Soc. 2020, 62, 102412. [CrossRef] · DOI
  24. Wang, Y.; Xu, R.; Zhou, C.; Kang, X.; Chen, Z. Digital twin and cloud-side-end collaboration for intelligent battery management system. J. Manuf. Syst. 2022, 62, 124-134. [CrossRef] · DOI
  25. Granacher, J.; Nguyen, T.V.; Castro-Amoedo, R.; Maréchal, F. Overcoming decision paralysis-A digital twin for decision making in energy system design. Appl. Energy 2022, 306, 117954. [CrossRef] · DOI
  26. Olatunji, O.O.; Adedeji, P.A.; Madushele, N.; Jen, T.C. Overview of Digital Twin Technology in Wind Turbine Fault Diagnosis and Condition Monitoring. In Proceedings of the 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT), Cape Town, South Africa, 13-15 May 2021; pp. 201-207. [CrossRef] · DOI
  27. You, M.; Wang, Q.; Sun, H.; Castro, I.; Jiang, J. Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties. Appl. Energy 2022, 305, 117899. [CrossRef] · DOI · Paper page
  28. Darbali-Zamora, R.; Johnson, J.; Summers, A.; Jones, C.B.; Hansen, C.; Showalter, C. State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin. Energies 2021, 14, 774. [CrossRef] · DOI
  29. Kohne, T.; Burkhardt, M.; Theisinger, L.; Weigold, M. Technical and digital twin concept of an industrial heat transfer station for low exergy waste heat. Proced. CIRP 2021, 104, 223-228. [CrossRef] · DOI
  30. Sleiti, A.K.; Kapat, J.S.; Vesely, L. Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems. Energy Rep. 2022, 8, 3704-3726. [CrossRef] · DOI
  31. Cioara, T.; Anghel, I.; Antal, M.; Salomie, I.; Antal, C.; Ioan, A.G. An Overview of Digital Twins Application Domains in Smart Energy Grid. To Be Submitted to an IEEE Conference. 2021. Available online: https://arxiv.org/abs/2104.07904 (accessed on 10 June 2022). · arXiv
  32. Kaiblinger, A.; Woschank, M. State of the Art and Future Directions of Digital Twins for Production Logistics: A Systematic Literature Review. Appl. Sci. 2022, 12, 669. [CrossRef] · DOI
  33. Mosavi, A.; Salimi, M.; Faizollahzadeh Ardabili, S.; Rabczuk, T.; Shamshirband, S.; Varkonyi-Koczy, A.R. State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. Energies 2019, 12, 1301. [CrossRef] · DOI
  34. Weigel, P.; Fischedick, M. Review and Categorization of Digital Applications in the Energy Sector. Appl. Sci. 2019, 9, 5350. [CrossRef] · DOI
  35. Brosinsky, C.; Krebs, R.; Westermann, D. Embedded Digital Twins in future energy management systems: Paving the way for automated grid control. Automatisierungstechnik 2020, 68, 750-764. [CrossRef] · DOI
  36. Stanelyte, D.; Radziukyniene, N.; Radziukynas, V. Overview of Demand-Response Services: A Review. Energies 2022, 15, 1659. [CrossRef] · DOI
  37. Wang, X.; Palazoglu, A.; El-Farra, N.H. Operational optimization and demand response of hybrid renewable energy systems. Appl. Energy 2015, 143, 324-335. [CrossRef] · DOI
  38. Halmschlager, D.; Beck, A.; Knöttner, S.; Koller, M.; Hofmann, R. Combined optimization for retrofitting of heat recovery and thermal energy supply in industrial systems. Appl. Energy 2022, 305, 117820. [CrossRef] · DOI
  39. Tzanis, N.; Andriopoulos, N.; Magklaras, A.; Mylonas, E.; Birbas, M.; Birbas, A. A Hybrid Cyber Physical Digital Twin Approach for Smart Grid Fault Prediction. In Proceedings of the 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Tampere, Finland, 10-12 June 2020; Volume 1, pp. 393-397. [CrossRef] · DOI
  40. Macek, K.; Endel, P.; Cauchi, N.; Abate, A. Long-term predictive maintenance: A study of optimal cleaning of biomass boilers. Energy Build. 2017, 150, 111-117. [CrossRef] · DOI
  41. Fox, H.; Pillai, A.C.; Friedrich, D.; Collu, M.; Dawood, T.; Johanning, L. A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance. Energies 2022, 15, 504. [CrossRef] · DOI
  42. Liu, K.; Wang, Y.; Lai, X. Data Science-Based Full-Lifespan Management of Lithium-Ion Battery: Manufacturing, Operation and Reutilization; Springer: Berlin/Heidelberg, Germany, 2022. [CrossRef] · DOI
  43. Wang, Y.; Kang, X.; Chen, Z. A Survey of Digital Twin Techniques in Smart Manufacturing and Management of Energy Applications. Green Energy Intell. Transp. 2022, 100014. [CrossRef] · DOI · Paper page
  44. Ardebili, A.A.; Longo, A.; Ficarella, A. Digital Twin (DT) in Smart Energy Systems-Systematic Literature Review of DT as a growing solution for Energy Internet of the Things (EIoT). E3S Web Conf. 2021, 312, 09002. [CrossRef] · DOI
  45. Zhang, H.; Liu, Q.; Chen, X.; Zhang, D.; Leng, J. A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line. IEEE Access 2017, 5, 26901-26911. [CrossRef] · DOI · Paper page
  46. Zečević, N. Energy intensification of steam methane reformer furnace in ammonia production by application of digital twin concept. Int. J. Sustain. Energy 2022, 41, 12-28. [CrossRef] · DOI
  47. Min, Q.; Lu, Y.; Liu, Z.; Su, C.; Wang, B. Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry. Int. J. Inf. Manag. 2019, 49, 502-519. [CrossRef] · DOI · Paper page
  48. Prawiranto, K.; Carmeliet, J.; Defraeye, T. Physics-Based Digital Twin Identifies Trade-Offs Between Drying Time, Fruit Quality, and Energy Use for Solar Drying. Front. Sustain. Food Syst. 2021, 4, 606845. [CrossRef] · DOI
  49. Yu, J.; Liu, P.; Li, Z. Hybrid modelling and digital twin development of a steam turbine control stage for online performance monitoring. Renew. Sustain. Energy Rev. 2020, 133, 110077. [CrossRef] · DOI
  50. Developing a Digital Twin: The Roadmap for Oil and Gas Optimization. In Proceedings of the SPE Offshore Europe Conference and Exhibition, Aberdeen, UK, 3-6 September 2019; Volume Day 1 Tue. [CrossRef] · DOI
  51. Zabala, L.; Febres, J.; Sterling, R.; López, S.; Keane, M. Virtual testbed for model predictive control development in district cooling systems. Renew. Sustain. Energy Rev. 2020, 129, 109920. [CrossRef] · DOI
  52. Abdrakhmanova, K.N.; Fedosov, A.V.; Idrisova, K.R.; Abdrakhmanov, N.K.; Valeeva, R.R. Review of modern software complexes and digital twin concept for forecasting emergency situations in oil and gas industry. IOP Conf. Ser. Mater. Sci. Eng. 2020, 862, 032078. [CrossRef] · DOI
  53. Weigel, P.; Fischedick, M.; Viebahn, P. Holistic Evaluation of Digital Applications in the Energy Sector-Evaluation Framework Development and Application to the Use Case Smart Meter Roll-Out. Sustainability 2021, 13, 6834. [CrossRef] · DOI
  54. De Lauretis, L. From Monolithic Architecture to Microservices Architecture. In Proceedings of the 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), Berlin, Germany, 28-31 October 2019; pp. 93-96. [CrossRef] · DOI
  55. Steindl, G.; Stagl, M.; Kasper, L.; Kastner, W.; Hofmann, R. Generic Digital Twin Architecture for Industrial Energy Systems. Appl. Sci. 2020, 10, 8903. [CrossRef] · DOI
  56. Perno, M.; Hvam, L.; Haug, A. Implementation of digital twins in the process industry: A systematic literature review of enablers and barriers. Comput. Ind. 2022, 134, 103558. [CrossRef] · DOI
  57. Perno, M.; Hvam, L.; Haug, A. Enablers and Barriers to the Implementation of Digital Twins in the Process Industry: A Systematic Literature Review. In Proceedings of the 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, 14-17 September 2020; pp. 959-964. [CrossRef] · DOI
  58. Boje, C.; Guerriero, A.; Kubicki, S.; Rezgui, Y. Towards a semantic Construction Digital Twin: Directions for future research. Autom. Constr. 2020, 114, 103179. [CrossRef] · DOI
  59. Mabkhot, M.M.; Al-Ahmari, A.M.; Salah, B.; Alkhalefah, H. Requirements of the Smart Factory System: A Survey and Perspective. Machines 2018, 6, 23. [CrossRef] · DOI
  60. Moyne, J.; Qamsane, Y.; Balta, E.C.; Kovalenko, I.; Faris, J.; Barton, K.; Tilbury, D.M. A Requirements Driven Digital Twin Framework: Specification and Opportunities. IEEE Access 2020, 8, 107781-107801. [CrossRef] · DOI
  61. Weskamp, J.N.; Ghosh Chowdhury, A.; Pethig, F.; Wisniewski, L. Architecture for Knowledge Exploration of Industrial Data for Integration into Digital Services. In Proceedings of the 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Tampere, Finland, 10-12 June 2020; Volume 1, pp. 98-104. [CrossRef] · DOI
  62. Tao, F.; Zhang, M.; Nee, A.Y.C. Chapter 3-Five-Dimension Digital Twin Modeling and Its Key Technologies. In Digital Twin Driven Smart Manufacturing;
  63. Tao, F., Zhang, M., Nee, A.Y.C., Eds.; Academic Press: Cambridge, MA, USA, 2019; pp. 63-81. [CrossRef] · DOI
  64. Assad Neto, A.; Ribeiro da Silva, E.; Deschamps, F.; Pinheiro de Lima, E. Digital twins in manufacturing: An assessment of key features. Proced. CIRP 2021, 97, 178-183. [CrossRef] · DOI
  65. Gorecky, D.; Schmitt, M.; Loskyll, M.; Zühlke, D. Human-machine-interaction in the industry 4.0 era. In Proceedings of the 2014 12th IEEE International Conference on Industrial Informatics (INDIN), Porto Alegre, Brazil, 27-30 July 2014; pp. 289-294. [CrossRef] · DOI
  66. Gehrmann, C.; Gunnarsson, M. A Digital Twin Based Industrial Automation and Control System Security Architecture. IEEE Trans. Ind. Inform. 2020, 16, 669-680. [CrossRef] · DOI
  67. Semeraro, C.; Lezoche, M.; Panetto, H.; Dassisti, M. Digital twin paradigm: A systematic literature review. Comput. Ind. 2021, 130, 103469. [CrossRef] · DOI
  68. Massel, L.V.; Massel, A.G. Development Of Digital Twins And Digital Shadows of Energy Objects And Systems Using Scientific Tools For Energy Research. E3S Web Conf. 2020, 209, 2019. [CrossRef] · DOI
  69. Kohne, T.; Theisinger, L.; Scherff, J.; Weigold, M. Data and optimization model of an industrial heat transfer station to increase energy flexibility. Energy Inform. 2021, 4, 1-17. [CrossRef] · DOI
  70. Stark, R.; Damerau, T. Digital Twin. In CIRP Encyclopedia of Production Engineering; Springer: Berlin/Heidelberg, Germany, 2019; pp. 1-8. [CrossRef] · DOI
  71. Abburu, S.; Berre, A.J.; Jacoby, M.; Roman, D.; Stojanovic, L.; Stojanovic, N. COGNITWIN-Hybrid and Cognitive Digital Twins for the Process Industry. In Proceedings of the 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Cardiff, UK, 15-17 June 2020; pp. 1-8. [CrossRef] · DOI
  72. Zheng, Y.; Yang, S.; Cheng, H. An application framework of digital twin and its case study. J. Ambient. Intell. Humaniz. Comput. 2019, 10, 1141-1153. [CrossRef] · DOI
  73. Lee, J.; Bagheri, B.; Kao, H.A. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett. 2015, 3, 18-23. [CrossRef] · DOI · Paper page
  74. Bouter, C.; Pourjafarian, M.; Simar, L.; Wilterdink, R. Towards a Comprehensive Methodology for Modelling Submodels in the Industry 4.0 Asset Administration Shell. In Proceedings of the 2021 IEEE 23rd Conference on Business Informatics (CBI), Bolzano, Italy, 1-3 September 2021; Volume 2, pp. 10-19. [CrossRef] · DOI
  75. Jacoby, M.; Volz, F.; Weißenbacher, C.; Stojanovic, L.; Usländer, T. An approach for Industrie 4.0-compliant and data-sovereign Dig- ital Twins: Realization of the Industrie 4.0 Asset Administration Shell with a data-sovereignty extension. Automatisierungstechnik 2021, 69, 1051-1061. [CrossRef] · DOI
  76. Bader, S.R.; Maleshkova, M. The Semantic Asset Administration Shell. In Proceedings of the Semantic Systems. The Power of AI and Knowledge Graphs, Karlsruhe, Germany, 9-12 September 2019;
  77. Acosta, M., Cudré-Mauroux, P., Maleshkova, M., Pellegrini, T., Sack, H., Sure-Vetter, Y., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 159-174. [CrossRef] · DOI
  78. Al-Ali, A.R.; Gupta, R.; Zaman Batool, T.; Landolsi, T.; Aloul, F.; Al Nabulsi, A. Digital Twin Conceptual Model within the Context of Internet of Things. Future Internet 2020, 12, 163. [CrossRef] · DOI
  79. Human, C.; Basson, A.H.; Kruger, K. Digital Twin Data Pipeline Using MQTT in SLADTA. In Proceedings of the International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing, Paris, France, 1-2 October 2020; Springer: Berlin/Heidelberg, Germany, 2020; pp. 111-122. [CrossRef] · DOI
  80. Naik, N. Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP. In Proceedings of the 2017 IEEE International Systems Engineering Symposium (ISSE), Vienna, Austria, 11-13 October 2017; pp. 1-7. [CrossRef] · DOI · Paper page
  81. Profanter, S.; Tekat, A.; Dorofeev, K.; Rickert, M.; Knoll, A. OPC UA versus ROS, DDS, and MQTT: Performance Evaluation of Industry 4.0 Protocols. In Proceedings of the 2019 IEEE International Conference on Industrial Technology (ICIT), Melbourne, Australia, 13-15 February 2019; pp. 955-962. [CrossRef] · DOI
  82. Data Flow and Communication Framework Supporting Digital Twin for Geometry Assurance, Volume 2: Advanced Manu- facturing. In Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Tampa, FL, USA, 3-9 November 2017. [CrossRef] · DOI
  83. Ehrlinger, L.; Wöß, W. Towards a Definition of Knowledge Graphs. SEMANTiCS Posters Demos SuCCESS 2016, 48, 2. 100. Schachinger, D.; Kastner, W.; Gaida, S. Ontology-based abstraction layer for smart grid interaction in building energy management systems. In Proceedings of the 2016 IEEE International Energy Conference (ENERGYCON), Leuven, Belgium, 4-8 April 2016; pp. 1-6. [CrossRef] 101. Steindl, G.; Kastner, W. Query Performance Evaluation of Sensor Data Integration Methods for Knowledge Graphs. In Proceedings of the 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE), Sofia, Bulgaria, 21-22 November 2019; pp. 1-8. [CrossRef] 102. · DOI
  84. Frühwirth, T.; Kastner, W.; Krammer, L. A methodology for creating reusable ontologies. In Proceedings of the 2018 IEEE Industrial Cyber-Physical Systems (ICPS), Saint Petersburg, Russia, 15-18 May 2018; pp. 65-70. [CrossRef] 103. · DOI
  85. Steindl, G.; Kastner, W. Ontology-Based Model Identification of Industrial Energy Systems. In Proceedings of the 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), Delft, The Netherlands, 17-19 June 2020; pp. 1217-1223. [CrossRef] 104. Janowicz, K.; Haller, A.; Cox, S.J.; Le Phuoc, D.; Lefrançois, M. SOSA: A lightweight ontology for sensors, observations, samples, and actuators. J. Web Semant. 2019, 56, 1-10. [CrossRef] 105. Küçük, D.; Salor, Ö.; Inan, T.; Çadırcı, I.; Ermiş, M. PQONT: A domain ontology for electrical power quality. Adv. Eng. Inform. 2010, 24, 84-95. [CrossRef] 106. Devanand, A.; Karmakar, G.; Krdzavac, N.; Rigo-Mariani, R.; Foo Eddy, Y.; Karimi, I.A.; Kraft, M. OntoPowSys: A power system ontology for cross domain interactions in an eco industrial park. Energy AI 2020, 1, 100008. [CrossRef] 107. Booshehri, M.; Emele, L.; Flügel, S.; Förster, H.; Frey, J.; Frey, U.; Glauer, M.; Hastings, J.; Hofmann, C.; Hoyer-Klick, C.; et al. Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy AI 2021, 5, 100074. [CrossRef] 108. Morbach, J.; Wiesner, A.; Marquardt, W. OntoCAPE-A (re)usable ontology for computer-aided process engineering. Comput. Chem. Eng. 2009, 33, 1546-1556. [CrossRef] 109. Devanand, A.; Zhou, L.; Karimi, I.A.; Kraft, M. An Ontology Based Cyber-infrastructure for the Development of Smart Eco Industrial Parks. In Proceedings of the 13th International Symposium on Process Systems Engineering (PSE 2018), San Diego, CA, USA, 1-5 July 2018; · DOI
  86. Eden, M.R., Ierapetritou, M.G., Towler, G.P., Eds.; Elsevier: Amsterdam, The Netherlands, 2018; Volume 44, pp. 2047-2052. [CrossRef] 110. Martin, D.; Burstein, M.; Hobbs, J.; Lassila, O.; McDermott, D.; McIlraith, S.; Narayanan, S.; Paolucci, M.; Parsia, B.; Payne, T.; et al. OWL-S: Semantic markup for web services. W3C Memb. Submiss. 2004, 22. Available online: https://www.w3.org/Submission/ OWL-S/ (accessed on 10 June 2022). 111. · DOI
  87. Hobbs, J.; Little, C. Time Ontology in OWL. W3C Candidate Recommendation. World Wide Web Consort. 2020, 27, 3-36. 112. Mitterhofer, M.; Schneider, G.F.; Stratbücker, S.; Sedlbauer, K. An FMI-enabled methodology for modular building performance simulation based on Semantic Web Technologies. Build. Environ. 2017, 125, 49-59. [CrossRef] 113. Publio, G.C.; Esteves, D.; Ławrynowicz, A.; Panov, P.; Soldatova, L.; Soru, T.; Vanschoren, J.; Zafar, H. ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies. arXiv 2018, arXiv:1807.05351. 114. · DOI
  88. Ocker, F.; Vogel-Heuser, B.; Paredis, C.J. A framework for merging ontologies in the context of smart factories. Comput. Ind. 2022, 135, 103571. [CrossRef] 115. Richardson, C. Microservices Patterns: With Examples in Java; Manning: Hong Kong, China, 2018. 116. Steindl, G. Digital Twin Service Framework. 2021. Available online: https://github.com/Smart-Industrial-Concept/ DigitalTwinServiceFramework (accessed on 25 January 2022). 117. Fernández-López, M.; Gómez-Pérez, A.; Juristo, N. METHONTOLOGY: From Ontological Art Towards Ontological Engineering. In Proceedings of the Ontological Engineering AAAI-97 Spring Symposium Series, Stanford, CA, USA, 24-26 March 1997; American Asociation for Artificial Intelligence: Menlo Park, CA, USA, 1997; pp. 24-26. Available online: http://oa.upm.es/5484 (accessed on 10 June 2022). · DOI

Cited by

Sorted by citations

Authors
Kasper, L., Birkelbach, F., Schwarzmayr, P., Steindl, G., Ramsauer, D., & Hofmann, R.
Affiliations
TU Wien (AT)
Corresponding author
Kasper, Lukas
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2022-07-10
Volume
12
Issue
14
Pages
6981
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app12146981
OpenAlex
W4285014818
Cited by
33
References
14
Citations by year
2026: 4 · 2025: 12 · 2024: 8 · 2023: 7 · 2022: 2
FWCI
3.33
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • reposiTUm (TU Wien) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Austrian Climate and Energy Fund’s initiative Energieforschung (e!MISSION) 6th call (881140)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Power Systems and Technologies
Keywords
digital twin, industrial energy systems, digital twin platform, reference architecture, implementation barriers, digitalization of energy, service engineering, multi-time-scale systems, standardization

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