Design and research of intelligent watt hour meter fault early warning system based on data mining technology

  • Taorong Wang Urban Vocational College of Sichuan, Chengdu 610000, China
  • Zhengang Shi State Grid HeBei Marketing Service Center, Shijiazhuang 050000, China
  • Tao Peng State Grid HeBei Marketing Service Center, Shijiazhuang 050000, China
  • Linhao Zhang State Grid HeBei Marketing Service Center, Shijiazhuang 050000, China
  • Bo Gao State Grid HeBei Marketing Service Center, Shijiazhuang 050000, China
  • Hongxi Wang State Grid HeBei Marketing Service Center, Shijiazhuang 050000, China
Keywords: big data; data mining technology; intelligent electricity meter; fault early warning system; data and network management
Ariticle ID: 557

Abstract

With the rapid development of information technology and the continuous improvement of communication technology, electric energy meters are innovating and developing towards networking, informatization and intelligence. This research constructs a model design of electric energy failure early warning system based on big data mining technology under the background of big data. It analyzes and studies the intelligent electric energy meter failure early warning system. Through the analysis and comparison of the factors affected, comprehensive performance and application prospects of the intelligent electric energy meter failure early warning system under different data mining technologies, the research results show that the application of big data mining technology makes the heavy work of data collection and data analysis integrate big data mining technology, it can be popularized more widely and applied to more scenarios, thus reducing the manual workload, it makes the work efficiency more significantly improved, and can promote the reform of China’s smart grid more quickly.

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Published
2024-11-19
How to Cite
Wang, T., Shi, Z., Peng, T., Zhang, L., Gao, B., & Wang, H. (2024). Design and research of intelligent watt hour meter fault early warning system based on data mining technology. Molecular & Cellular Biomechanics, 21(3), 557. https://doi.org/10.62617/mcb557
Section
Article