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Research on business collaborative optimization of power grid en-terprises based on data mining algorithm

Published: 01 June 2024 Publication History

Abstract

With the rapid development of information technology and the growing power industry, power grid enterprises are facing a more complex and competitive market environment. In this context, business collaborative optimization has become a key challenge for grid enterprises to improve efficiency, reduce costs and provide a more reliable power supply. The topic of this study is "Business collaboration optimization of power grid enterprises based on data mining algorithm", which aims to explore how to use data mining algorithm to improve the business collaboration efficiency of power grid enterprises. Firstly, this study reviews the importance of business collaboration among power grid enterprises and the status quo of related research. Then, we introduce the potential application of data mining algorithm in the power industry, and elaborate how data mining technology can be applied to the business collaborative optimization of power grid enterprises. By analyzing and integrating big data from various business areas, we are able to identify potential efficiency improvement opportunities, optimize resource allocation, improve the reliability of the power system, and reduce operating costs. In summary, this study provides an innovative method for power grid enterprise business collaborative optimization, and emphasizes the key role of data mining algorithms in the power industry. This study provides powerful tools and theoretical support for power grid enterprises to achieve sustainable development in the ever-changing market, and also provides a valuable reference for the research in related fields.[1]

References

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Zhang, Y., & Wang, X. 2017. Data Mining-Based Energy Consumption Forecasting for Smart Grids. IEEE Transactions on Industrial Informatics, 13(4), 2086-2095.
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Wei, X., & Liu, Z. 2018. A Data Mining-Based Approach for Load Forecasting in Smart Grids. IEEE Transactions on Industrial Informatics, 14(9), 4149-4156.
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    AISNS '23: Proceedings of the 2023 International Conference on Artificial Intelligence, Systems and Network Security
    December 2023
    467 pages
    ISBN:9798400716966
    DOI:10.1145/3661638
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 01 June 2024

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