IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i18p10369-d637204.html
   My bibliography  Save this article

Industry 4.0 Contribution to Asset Management in the Electrical Industry

Author

Listed:
  • Gabrielle Biard

    (Department of Industrial Engineering, University of Quebec in Trois-Rivieres, Trois-Rivieres, QC G8Z 4M3, Canada)

  • Georges Abdul Nour

    (Department of Industrial Engineering, University of Quebec in Trois-Rivieres, Trois-Rivieres, QC G8Z 4M3, Canada)

Abstract

Industry 4.0 has revolutionized paradigms by leading to major technological developments in several sectors, including the energy sector. Aging equipment fleets and changing demand are challenges facing electricity companies. Forced to limit resources, these organizations must question their method and the current model of asset management (AM). The objective of this article is to detail how industry 4.0 can improve the AM of electrical networks from a global point of view. To do so, the industry 4.0 tools will be presented, as well as a review of the literature on their application and benefits in this area. From the literature review conducted, we observe that once properly structured and managed, big data forms the basis for the implementation of advanced tools and technologies in electrical networks. The data generated by smart grids and data compiled for several years in electrical networks have the characteristics of big data. Therefore, it leaves room for a multitude of possibilities for comprehensive analysis and highly relevant information. Several tools and technologies, such as modeling, simulation as well as the use of algorithms and IoT, combined with big data analysis, leads to innovations that serve a common goal. They facilitate the control of reliability-related risks, maximize the performance of assets, and optimize the intervention frequency. Consequently, they minimize the use of resources by helping decision-making processes.

Suggested Citation

  • Gabrielle Biard & Georges Abdul Nour, 2021. "Industry 4.0 Contribution to Asset Management in the Electrical Industry," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10369-:d:637204
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/18/10369/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/18/10369/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Juliana Salvadorinho & Leonor Teixeira, 2021. "Stories Told by Publications about the Relationship between Industry 4.0 and Lean: Systematic Literature Review and Future Research Agenda," Publications, MDPI, vol. 9(3), pages 1-20, July.
    2. Elizaveta Gavrikova & Irina Volkova & Yegor Burda, 2020. "Strategic Aspects of Asset Management: An Overview of Current Research," Sustainability, MDPI, vol. 12(15), pages 1-31, July.
    3. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    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. Jihoon Moon & Junhong Kim & Pilsung Kang & Eenjun Hwang, 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods," Energies, MDPI, vol. 13(4), pages 1-37, February.
    2. Zhou, Kaile & Yang, Changhui & Shen, Jianxin, 2017. "Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China," Utilities Policy, Elsevier, vol. 44(C), pages 73-84.
    3. Bertha Leticia Treviño-Elizondo & Heriberto García-Reyes & Rodrigo E. Peimbert-García, 2023. "A Maturity Model to Become a Smart Organization Based on Lean and Industry 4.0 Synergy," Sustainability, MDPI, vol. 15(17), pages 1-24, September.
    4. Cen, Xiao & Chen, Zengliang & Chen, Haifeng & Ding, Chen & Ding, Bo & Li, Fei & Lou, Fangwei & Zhu, Zhenyu & Zhang, Hongyu & Hong, Bingyuan, 2024. "User repurchase behavior prediction for integrated energy supply stations based on the user profiling method," Energy, Elsevier, vol. 286(C).
    5. Damjan Maletič & Matjaž Maletič & Basim Al-Najjar & Boštjan Gomišček, 2020. "An Analysis of Physical Asset Management Core Practices and Their Influence on Operational Performance," Sustainability, MDPI, vol. 12(21), pages 1-20, October.
    6. Jia, Kunqi & Guo, Ge & Xiao, Jucheng & Zhou, Huan & Wang, Zhihua & He, Guangyu, 2019. "Data compression approach for the home energy management system," Applied Energy, Elsevier, vol. 247(C), pages 643-656.
    7. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    8. Čábelková, Inna & Strielkowski, Wadim & Streimikiene, Dalia & Cavallaro, Fausto & Streimikis, Justas, 2021. "The social acceptance of nuclear fusion for decision making towards carbon free circular economy: Evidence from Czech Republic," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    9. Amin, Amin & Mourshed, Monjur, 2024. "Community stochastic domestic electricity forecasting," Applied Energy, Elsevier, vol. 355(C).
    10. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.
    11. Stefano Villa & Claudio Sassanelli, 2020. "The Data-Driven Multi-Step Approach for Dynamic Estimation of Buildings’ Interior Temperature," Energies, MDPI, vol. 13(24), pages 1-23, December.
    12. Papa, Armando & Mital, Monika & Pisano, Paola & Del Giudice, Manlio, 2020. "E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    13. Seung-Mo Je & Hyeyoung Ko & Jun-Ho Huh, 2021. "Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant," Energies, MDPI, vol. 14(21), pages 1-31, October.
    14. Liu, Bo & Hou, Yufan & Luan, Wenpeng & Liu, Zishuai & Chen, Sheng & Yu, Yixin, 2023. "A divide-and-conquer method for compression and reconstruction of smart meter data," Applied Energy, Elsevier, vol. 336(C).
    15. 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).
    16. Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
    17. Kaitlin Kish, 2020. "Paying Attention: Big Data and Social Advertising as Barriers to Ecological Change," Sustainability, MDPI, vol. 12(24), pages 1-17, December.
    18. Yazdanie, M. & Orehounig, K., 2021. "Advancing urban energy system planning and modeling approaches: Gaps and solutions in perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    19. Francesco Cappa & Stefano Franco & Federica Rosso, 2022. "Citizens and cities: Leveraging citizen science and big data for sustainable urban development," Business Strategy and the Environment, Wiley Blackwell, vol. 31(2), pages 648-667, February.
    20. Anand Krishnan Prakash & Susu Xu & Ram Rajagopal & Hae Young Noh, 2018. "Robust Building Energy Load Forecasting Using Physically-Based Kernel Models," Energies, MDPI, vol. 11(4), pages 1-21, April.

    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:jsusta:v:13:y:2021:i:18:p:10369-:d:637204. 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.