IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1444-d1053874.html
   My bibliography  Save this article

A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies

Author

Listed:
  • Jan Schweikert

    (Institute of Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany)

  • Karl-Uwe Stucky

    (Institute of Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany)

  • Wolfgang Süß

    (Institute of Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany)

  • Veit Hagenmeyer

    (Institute of Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany)

Abstract

Smart grids of the future will create and provide huge data volumes, which are subject to FAIR (Findable, Accessible, Interoperable, and Reusable) data management solutions when used within the scientific domain and for operation. FAIR Digital Objects (FDOs) provide access to (meta)data, and ontologies explicitly describe metadata as well as application data objects and domains. The present paper proposes a novel approach to integrate FAIR digital objects and ontologies as metadata models in order to support data access for energy researchers, energy research applications, operational applications and energy information systems. As the first example domain to be modeled using an ontology and to get integrated with FAIR digital objects, a photovoltaic (PV) system model is selected. For the given purpose, a discussion of existing energy ontologies shows the necessity to develop a new PV ontology. By integration of FDOs, this new PV ontology is introduced in the present paper. Furthermore, the concept of FDOs is integrated with the PV ontology in such a way that it allows for generalization. By this, the present paper contributes to a sustainable data management for smart grid operation, especially for interoperability, by using ontologies and, hence, unambiguous semantics. An information system application that visualizes the PV system, its describing data and collected sensor data, is proposed. As a proof of concept the details of the use case implementation are presented.

Suggested Citation

  • Jan Schweikert & Karl-Uwe Stucky & Wolfgang Süß & Veit Hagenmeyer, 2023. "A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies," Energies, MDPI, vol. 16(3), pages 1-21, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1444-:d:1053874
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1444/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/3/1444/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Koenraad De Smedt & Dimitris Koureas & Peter Wittenburg, 2020. "FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units," Publications, MDPI, vol. 8(2), pages 1-17, April.
    2. Nicola Guarino & Daniel Oberle & Steffen Staab, 2009. "What Is an Ontology?," International Handbooks on Information Systems, in: Steffen Staab & Rudi Studer (ed.), Handbook on Ontologies, pages 1-17, Springer.
    3. Hyun Joong Kim & Chang Min Jeong & Jin-Man Sohn & Jhi-Young Joo & Vaibhav Donde & Youngmi Ko & Yong Tae Yoon, 2020. "A Comprehensive Review of Practical Issues for Interoperability Using the Common Information Model in Smart Grids," Energies, MDPI, vol. 13(6), pages 1-20, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hai Zhu & Xingsi Xue & Hongfeng Wang, 2022. "Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix," Mathematics, MDPI, vol. 10(12), pages 1-12, June.
    2. Salvatore F. Pileggi & Marius Indorf & Ayman Nagi & Wolfgang Kersten, 2020. "CoRiMaS—An Ontological Approach to Cooperative Risk Management in Seaports," Sustainability, MDPI, vol. 12(11), pages 1-23, June.
    3. Greig Charnock & Hug March & Ramon Ribera-Fumaz, 2021. "From smart to rebel city? Worlding, provincialising and the Barcelona Model," Urban Studies, Urban Studies Journal Limited, vol. 58(3), pages 581-600, February.
    4. Shuo Chen & Falko Ebe & Jeromie Morris & Heiko Lorenz & Christoph Kondzialka & Gerd Heilscher, 2022. "Implementation and Test of an IEC 61850-Based Automation Framework for the Automated Data Model Integration of DES (ADMID) into DSO SCADA," Energies, MDPI, vol. 15(4), pages 1-30, February.
    5. Xingsi Xue & Qi Wu & Miao Ye & Jianhui Lv, 2022. "Efficient Ontology Meta-Matching Based on Interpolation Model Assisted Evolutionary Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-20, September.
    6. Parastoo Delgoshaei & Mohammad Heidarinejad & Mark A. Austin, 2022. "A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    7. Xixi Zhu & Bin Liu & Cheng Zhu & Zhaoyun Ding & Li Yao, 2023. "Approximate Reasoning for Large-Scale ABox in OWL DL Based on Neural-Symbolic Learning," Mathematics, MDPI, vol. 11(3), pages 1-24, January.
    8. R. B. K. Brown & G. Beydoun & G. Low & W. Tibben & R. Zamani & F. García-Sánchez & R. Martinez-Bejar, 2016. "Computationally efficient ontology selection in software requirement planning," Information Systems Frontiers, Springer, vol. 18(2), pages 349-358, April.
    9. Nguyen Hoang Thuan & Pedro Antunes & David Johnstone, 2018. "A Decision Tool for Business Process Crowdsourcing: Ontology, Design, and Evaluation," Group Decision and Negotiation, Springer, vol. 27(2), pages 285-312, April.
    10. Runsheng Miao & Yuchen Huang & Zhenyu Zhang, 2023. "A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies," Mathematics, MDPI, vol. 11(14), pages 1-27, July.
    11. Henrik tom Wörden & Florian Spreckelsen & Stefan Luther & Ulrich Parlitz & Alexander Schlemmer, 2024. "Mapping Hierarchical File Structures to Semantic Data Models for Efficient Data Integration into Research Data Management Systems," Data, MDPI, vol. 9(2), pages 1-15, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1444-:d:1053874. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.