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Construction of Smart Grid Load Forecast Model by Edge Computing

Author

Listed:
  • Xudong Pang

    (Electrical Engineering Department, Yanshan University, Qinhuangdao 066000, China)

  • Xiangchen Lu

    (School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China)

  • Hao Ding

    (Electrical Engineering Department, Yanshan University, Qinhuangdao 066000, China)

  • Josep M. Guerrero

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

Abstract

This research aims to minimize the unnecessary resource consumption by intelligent Power Grid Systems (PGSs). Edge Computing (EC) technology is used to forecast PGS load and optimize the PGS load forecasting model. Following a literature review of EC and Internet of Things (IoT)-native edge devices, an intelligent PGS-oriented Resource Management Scheme (RMS) and PGS load forecasting model are proposed based on task offloading. Simultaneously, an online delay-aware power Resource Allocation Algorithm (RAA) is developed for EC architecture. Finally, comparing three algorithms corroborate that the system overhead decreases significantly with the model iteration. From the 40th iteration, the system overhead stabilizes. Moreover, given no more than 50 users, the average user delay of the proposed delay-aware power RAA is less than 13 s. The average delay of the proposed algorithm is better than that of the other two algorithms. This research contributes to optimizing intelligent PGS in smart cities and improving power transmission efficiency.

Suggested Citation

  • Xudong Pang & Xiangchen Lu & Hao Ding & Josep M. Guerrero, 2022. "Construction of Smart Grid Load Forecast Model by Edge Computing," Energies, MDPI, vol. 15(9), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3028-:d:798450
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    References listed on IDEAS

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    4. Moslem Akbari Vakilabadi & Abolfazl Afzalabadi & Alireza Khoeini Poorfar & Alireza Rahbari & Mokhtar Bidi & Mohammad Hossein Ahmadi & Tingzhen Ming, 2019. "Technical and economical evaluation of grid-connected renewable power generation system for a residential urban area," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 14(1), pages 10-22.
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