Prediction of substation maintenance and repair costs with improved GM (1,1) model
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(1.State Grid Hubei Economic and Technological Research Institute, Wuhan 430000,China;2.School of Electrical Engineering, Southeast University, Nanjing 210096,China;3. State Grid Hubei Electric Power Co., Ltd.,Wuhan 430000,China;4.School of Electrical & Information Engineering,Changsha University of Science & Technology, Changsha 410114,China)

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TM63

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    Abstract:

    Aiming at the fluctuation of maintenance and repair costs in substation life cycle cost, a traditional grey model and an improved grey model are adopted respectively, to forecast the maintenance and repair costs of a substation in the next 3 years in order to optimize cost allocation strategy. Simulation results show that the prediction accuracy of both models is one grade; while both the average relative error and the posterior error ratio of the improved model are lower than those of the traditional one. The prediction accuracy of the improved model is hence higher than that of the traditional one, and can be suitable to predict the maintenance and repair costs of a substation. Finally, the improved grey model is used to predict the maintenance and repair costs during 2019 to 2021 of a specified substation in a city.

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李智威,王依燃,张赵阳,王 巍,方 钊,孙利平,唐 欣.基于改进GM(1,1)模型的变电站检修运维费用预测[J].电力科学与技术学报英文版,2024,(1):218-224. LI Zhiwei, WANG Yiran, ZHANG Zhaoyang, WANG Wei, FANG Zhao, SUN Liping, TANG Xin. Prediction of substation maintenance and repair costs with improved GM (1,1) model[J]. Journal of Electric Power Science and Technology,2024,(1):218-224.

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  • Received:
  • Revised:
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  • Online: April 22,2024
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