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Power Network Scheduling Strategy Based on Federated Learning Algorithm in Edge Computing Environment

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Big Data and Security (ICBDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1796))

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Abstract

Distributed new energy consumption scenarios, such as photovoltaic, energy storage, charging stake, etc., are facing the needs of processing massive real-time data, large-scale distributed new energy and access to diverse loads. Based on the business characteristics such as business peak-valley dynamic change, network connection and time-delay differential demand of different business in energy and power business, a reasonable and effective integrated resource scheduling model of computing resources suitable for distributed new energy consumption scenario is studied to support power planning and dynamic dispatch application. In this article, we propose an arithmetic planning strategy based on federal learning. Specifically, we first introduce a computing priority network scheduling framework in edge cloud computing environments. Secondly, we process the absorption data of corresponding energy nodes by random forest algorithm, adjust the connection relationship between a large number of internal nodes, control and dispatch the nodes, and then conduct integrated training through federal learning to dispatch the computing power of the overall network, so as to achieve fast and accurate algorithm dispatch. Then, under the same environment conditions, the simulation experiments of deep neural network and random forest algorithm are compared. A large number of simulation results show that the system can effectively assist the smart grid in reasonable algorithm dispatch and improve the resource utilization efficiency.

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Acknowledgment

This work was supported by the science and technology project of State Grid Jiangsu Electric Power Co., Ltd. under Grant No. J2022051.

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Authors and Affiliations

  1. State Grid Wuxi Power Supply Company, Wuxi, China

    Xiaowei Xu, Han Ding, Jiayu Wang & Liang Hua

Authors
  1. Xiaowei Xu
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  2. Han Ding
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  3. Jiayu Wang
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  4. Liang Hua
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Corresponding author

Correspondence to Xiaowei Xu .

Editor information

Editors and Affiliations

  1. Nanjing Institute of Technology, Nanjing, China

    Yuan Tian

  2. Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Tinghuai Ma

  3. Chinese Academy of Sciences, Shenzhen, China

    Qingshan Jiang

  4. Nanjing University of Information Science and Technology, Nanjing, China

    Qi Liu

  5. King Saud University, Riyadh, Saudi Arabia

    Muhammad Khurram Khan

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Xu, X., Ding, H., Wang, J., Hua, L. (2023). Power Network Scheduling Strategy Based on Federated Learning Algorithm in Edge Computing Environment. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_54

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  • DOI: https://doi.org/10.1007/978-981-99-3300-6_54

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  • Print ISBN: 978-981-99-3299-3

  • Online ISBN: 978-981-99-3300-6

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