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Intelligent Energy Management Systems in Industry 5.0: Cybersecurity Applications in Examples

Author

Listed:
  • Barbara Wyrzykowska

    (Institute of Management, Warsaw University of Life Sciences—SGGW, Nowoursynowska 166 St., 02-787 Warsaw, Poland)

  • Hubert Szczepaniuk

    (Institute of Management, Warsaw University of Life Sciences—SGGW, Nowoursynowska 166 St., 02-787 Warsaw, Poland)

  • Edyta Karolina Szczepaniuk

    (Polish Air Force University, Dywizjonu 303 Street No. 35, 08-521 Dęblin, Poland)

  • Anna Rytko

    (Institute of Economics and Finance, Warsaw University of Life Sciences—SGGW, Nowoursynowska 166 St., 02-787 Warsaw, Poland)

  • Marzena Kacprzak

    (Institute of Economics and Finance, Warsaw University of Life Sciences—SGGW, Nowoursynowska 166 St., 02-787 Warsaw, Poland)

Abstract

The article examines modern approaches to energy management in the context of the development of Industry 5.0 with a particular focus on cybersecurity. Key tenets of Industry 5.0 are discussed, including the integration of advanced technologies with intelligent energy management systems (IEMSs) and the growing need to protect data in the face of increasing cyber threats. The challenges faced by small and medium-sized enterprises (SMEs) using solutions based on renewable energy sources, such as photovoltaic farms, are also analyzed. The article presents examples of IEMS applications and discusses methods for securing these systems, offering an overview of cyber threat protection tools in the context of modern energy management. The analysis carried out provided information that will help businesses make rational decisions and contribute to shaping the state’s macroeconomic policy on cybersecurity and energy savings. The results of this research can also help develop more effective strategies for managing technology and IT infrastructure, which is crucial in the digital age of Industry 5.0.

Suggested Citation

  • Barbara Wyrzykowska & Hubert Szczepaniuk & Edyta Karolina Szczepaniuk & Anna Rytko & Marzena Kacprzak, 2024. "Intelligent Energy Management Systems in Industry 5.0: Cybersecurity Applications in Examples," Energies, MDPI, vol. 17(23), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5871-:d:1527384
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    References listed on IDEAS

    as
    1. Dhaou Said, 2023. "Quantum Computing and Machine Learning for Cybersecurity: Distributed Denial of Service (DDoS) Attack Detection on Smart Micro-Grid," Energies, MDPI, vol. 16(8), pages 1-11, April.
    2. Jin-Li Hu & Nhi Ha Bao Bui, 2024. "The Future Design of Smart Energy Systems with Energy Flexumers: A Constructive Literature Review," Energies, MDPI, vol. 17(9), pages 1-32, April.
    3. Sun, Wenqiang & Wang, Qiang & Zhou, Yue & Wu, Jianzhong, 2020. "Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives," Applied Energy, Elsevier, vol. 268(C).
    4. Seong-Kyu Kim & Jun-Ho Huh, 2018. "A Study on the Improvement of Smart Grid Security Performance and Blockchain Smart Grid Perspective," Energies, MDPI, vol. 11(8), pages 1-22, July.
    5. Fahad Alqahtani & Mohammed Almutairi & Frederick T. Sheldon, 2024. "Cloud Security Using Fine-Grained Efficient Information Flow Tracking," Future Internet, MDPI, vol. 16(4), pages 1-36, March.
    6. Nakkeeran Murugesan & Anantha Narayanan Velu & Bagavathi Sivakumar Palaniappan & Balamurugan Sukumar & Md. Jahangir Hossain, 2024. "Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid," Energies, MDPI, vol. 17(8), pages 1-34, April.
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