IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i5p1222-d1350805.html
   My bibliography  Save this article

Integrating Industry 4.0 and 5.0 Innovations for Enhanced Energy Management Systems

Author

Listed:
  • Vito Introna

    (Department of Enterprise Engineering, Tor Vergata University of Rome, 00133 Rome, Italy)

  • Annalisa Santolamazza

    (Department of Enterprise Engineering, Tor Vergata University of Rome, 00133 Rome, Italy)

  • Vittorio Cesarotti

    (Department of Enterprise Engineering, Tor Vergata University of Rome, 00133 Rome, Italy)

Abstract

Industry 4.0 and Industry 5.0 have introduced a lot of innovative technologies in industrial plants, transforming them into complex digital systems. On the other hand, the importance of Energy Management Systems in industrial plants is growing for both sustainability and economic reasons, but the opportunity of Industry 4.0/5.0 technologies in enhancing energy management systems is not fully understood. Thus, this paper analyzes how Industry 4.0/5.0 technologies can be applied to meet the requirements of Energy Management Systems, focusing on each aspect such as design, monitoring, control, and budget planning. It identifies additional opportunities that arise with different levels of technological implementation, suggesting organic implementation steps. The final aim is to provide a comprehensive framework for fostering a strategic and conscious implementation approach of these tools in the Energy Management Systems of industrial plants, giving clear and comprehensive suggestions.

Suggested Citation

  • Vito Introna & Annalisa Santolamazza & Vittorio Cesarotti, 2024. "Integrating Industry 4.0 and 5.0 Innovations for Enhanced Energy Management Systems," Energies, MDPI, vol. 17(5), pages 1-16, March.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1222-:d:1350805
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/5/1222/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/5/1222/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu, Wei & Patros, Panos & Young, Brent & Klinac, Elsa & Walmsley, Timothy Gordon, 2022. "Energy digital twin technology for industrial energy management: Classification, challenges and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Nikula, Riku-Pekka & Ruusunen, Mika & Leiviskä, Kauko, 2016. "Data-driven framework for boiler performance monitoring," Applied Energy, Elsevier, vol. 183(C), pages 1374-1388.
    3. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    4. Dalenogare, Lucas Santos & Benitez, Guilherme Brittes & Ayala, Néstor Fabián & Frank, Alejandro Germán, 2018. "The expected contribution of Industry 4.0 technologies for industrial performance," International Journal of Production Economics, Elsevier, vol. 204(C), pages 383-394.
    5. Salvatori, Simone & Benedetti, Miriam & Bonfà, Francesca & Introna, Vito & Ubertini, Stefano, 2018. "Inter-sectorial benchmarking of compressed air generation energy performance: Methodology based on real data gathering in large and energy-intensive industrial firms," Applied Energy, Elsevier, vol. 217(C), pages 266-280.
    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. Miriam Benedetti & Francesca Bonfà & Vito Introna & Annalisa Santolamazza & Stefano Ubertini, 2019. "Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications," Energies, MDPI, vol. 12(20), pages 1-28, October.
    2. Gilberto Santos & Jose Carlos Sá & Maria João Félix & Luís Barreto & Filipe Carvalho & Manuel Doiro & Kristína Zgodavová & Miladin Stefanović, 2021. "New Needed Quality Management Skills for Quality Managers 4.0," Sustainability, MDPI, vol. 13(11), pages 1-22, May.
    3. Michal Gluszak & Remigiusz Gawlik & Malgorzata Zieba, 2019. "Smart and Green Buildings Features in the Decision-Making Hierarchy of Office Space Tenants: An Analytic Hierarchy Process Study," Administrative Sciences, MDPI, vol. 9(3), pages 1-16, July.
    4. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    5. Tortorella, Guilherme Luz & Narayanamurthy, Gopalakrishnan & Thurer, Matthias, 2021. "Identifying pathways to a high-performing lean automation implementation: An empirical study in the manufacturing industry," International Journal of Production Economics, Elsevier, vol. 231(C).
    6. Ismael Cristofer Baierle & Francisco Tardelli da Silva & Ricardo Gonçalves de Faria Correa & Jones Luís Schaefer & Matheus Becker Da Costa & Guilherme Brittes Benitez & Elpidio Oscar Benitez Nara, 2022. "Competitiveness of Food Industry in the Era of Digital Transformation towards Agriculture 4.0," Sustainability, MDPI, vol. 14(18), pages 1-22, September.
    7. Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
    8. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    9. Olumide Emmanuel Oluyisola & Fabio Sgarbossa & Jan Ola Strandhagen, 2020. "Smart Production Planning and Control: Concept, Use-Cases and Sustainability Implications," Sustainability, MDPI, vol. 12(9), pages 1-29, May.
    10. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    11. Mohammadreza Akbari & John L. Hopkins, 2022. "Digital technologies as enablers of supply chain sustainability in an emerging economy," Operations Management Research, Springer, vol. 15(3), pages 689-710, December.
    12. Mykola Odrekhivskyi & Orysya Pshyk-Kovalska & Volodymyr Zhezhukha & Iryna Ivanochko, 2022. "Intelligent Management of Enterprise Business Processes," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
    13. Livio Cricelli & Serena Strazzullo, 2021. "The Economic Aspect of Digital Sustainability: A Systematic Review," Sustainability, MDPI, vol. 13(15), pages 1-15, July.
    14. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    15. Siqing Shan & Xin Wen & Yigang Wei & Zijin Wang & Yong Chen, 2020. "Intelligent manufacturing in industry 4.0: A case study of Sany heavy industry," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 679-690, July.
    16. Bai, Chunguang & Dallasega, Patrick & Orzes, Guido & Sarkis, Joseph, 2020. "Industry 4.0 technologies assessment: A sustainability perspective," International Journal of Production Economics, Elsevier, vol. 229(C).
    17. Bianco, Débora & Bueno, Adauto & Godinho Filho, Moacir & Latan, Hengky & Miller Devós Ganga, Gilberto & Frank, Alejandro G. & Chiappetta Jabbour, Charbel Jose, 2023. "The role of Industry 4.0 in developing resilience for manufacturing companies during COVID-19," International Journal of Production Economics, Elsevier, vol. 256(C).
    18. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Wuest, Thorsten & Stahre, Johan, 2020. "Smart Maintenance: a research agenda for industrial maintenance management," International Journal of Production Economics, Elsevier, vol. 224(C).
    19. Guilherme Luz Tortorella & Flavio S. Fogliatto & Michel J. Anzanello & Alejandro Mac Cawley Vergara & Roberto Vassolo & Jose Arturo Garza-Reyes, 2023. "Modeling the impact of industry 4.0 base technologies on the development of organizational learning capabilities," Operations Management Research, Springer, vol. 16(3), pages 1091-1104, September.
    20. Gemici, Evrim & Gemici, Zafer, 2021. "A Comparative Study on Turkey’s Science and Technology (S&T) Indicators," OSF Preprints csyud, Center for Open Science.

    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:17:y:2024:i:5:p:1222-:d:1350805. 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.