JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2025, Vol. 60 ›› Issue (9): 41-51.doi: 10.6040/j.issn.1671-9352.0.2024.377

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Topology construction and control based on long short-term prediction

YAN Li1, HU Hailin1, WANG Gaozhou1, ZHANG Wenbin1, PAN Fading1, ZHANG Xiao2, ZHENG Yanwei2*   

  1. 1. State Grid Shandong Electric Power Company Information and Communication Company, Jinan 250013, Shandong, China;
    2. School of Computer Science and Technology, Shandong University, Qingdao 266237, Shandong, China
  • Published:2025-09-10

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Abstract: To optimize dynamic network topology construction and resource allocation, a long short-term prediction-based topology construction and control(LSPTCC)framework is proposed. Long short-term memory(LSTM)network and Informer models are employed for long short-term prediction of multivariate time series. Temporal dependencies and non-stationary fluctuations in the data are accurately captured. Based on the prediction results, the enhanced capacity constrained design(ECCD)algorithm is used to construct a minimum spanning tree(MST)and to optimize the connections between nodes to minimize overall transmission losses. Additionally, a maximum network flow algorithm is applied to dynamically adjust resource allocation, ensuring efficient utilization of resources under fluctuating conditions. Experiments conducted on a photovoltaic consumption dataset demonstrate that the proposed framework can accurately predict power generation and consumption. The power transmission losses are effectively reduced by optimizing both the topology and resource allocation. The efficiency and robustness of the proposed algorithms are validated.

Key words: long short-term prediction, minimum spanning tree, maximum network flow algorithm, dynamic network topology

CLC Number: 

  • TP183
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