Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India

Research Article

Research on the Identification System of Power Big Data Attribute Entities based on Artificial Intelligence Algorithm

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  • @INPROCEEDINGS{10.4108/eai.17-11-2023.2342614,
        author={Jiangtao  Guo and Tianfu  Ma and Maihebubai  Xiaokaiti and Rui  Yin and Lulu  Liu},
        title={Research on the Identification System of Power Big Data Attribute Entities based on Artificial Intelligence Algorithm},
        proceedings={Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India},
        publisher={EAI},
        proceedings_a={ICSETPSD},
        year={2024},
        month={1},
        keywords={identification system recurrent neural networks classifying attribute entities},
        doi={10.4108/eai.17-11-2023.2342614}
    }
    
  • Jiangtao Guo
    Tianfu Ma
    Maihebubai Xiaokaiti
    Rui Yin
    Lulu Liu
    Year: 2024
    Research on the Identification System of Power Big Data Attribute Entities based on Artificial Intelligence Algorithm
    ICSETPSD
    EAI
    DOI: 10.4108/eai.17-11-2023.2342614
Jiangtao Guo1,*, Tianfu Ma1, Maihebubai Xiaokaiti1, Rui Yin1, Lulu Liu1
  • 1: State Grid Xinjiang Electric Power Co.,LTD., Information and Telecommunication Company, Urumqi, China
*Contact email: sgcc_guojiangtao@163.com

Abstract

This paper conducts a comprehensive study on the identification system of power big data attribute entities using artificial intelligence algorithms. The purpose of the study is to construct an effective system that can accurately classify and analyze attribute entities in power big data. The methodology involves data preprocessing, feature extraction, and algorithm selection, with a specific focus on Recurrent Neural Networks (RNNs). The RNN architecture, including the computation of hidden states, is detailed in the paper. The experiment is conducted on a relevant dataset, with appropriate evaluation metrics to assess the system's performance. The results validate the effectiveness of the proposed identification system, showcasing its accuracy and efficiency in classifying attribute entities. The discussion highlights the system's strengths, limitations, and avenues for future research. Overall, this research contributes to the field of power big data analysis and provides valuable insights for practitioners and researchers alike.