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Optimization Design of Electric Power Marketing System Based on Data Mining Technology

Published: 13 March 2023 Publication History

Abstract

Data mining technology has a wide range of applications in the current era, especially for electric power companies. As the power marketing decision-making system involves more data, there are often some cases of incomplete data collection and incorrect data analysis results in the system, and through the advantages of resources in the era of big data, power companies can focus on developing power system integration, centralised management of resources and big data decision support. In the process of data integration, the power grid company has developed a large number of information systems, but still lacks some practical enterprise decision-making systems. The so-called enterprise decision-making system is a decision-making system that facilitates customer service, improves efficiency and saves labour costs. This paper uses data warehousing and data mining technology to propose the development and design of a power marketing decision support system based on data mining technology, and designs the business architecture, application architecture, data architecture and technical architecture based on the results of requirement analysis. Through practice, it is shown that the system has strong management, decision-making and query capabilities, fast response speed and relatively simple operation, which can provide comprehensive information support for high-level marketing decisions and improve enterprise competitiveness and economic benefits.

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  1. Optimization Design of Electric Power Marketing System Based on Data Mining Technology

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    BDSIC '22: Proceedings of the 2022 4th International Conference on Big-data Service and Intelligent Computation
    November 2022
    87 pages
    ISBN:9781450397070
    DOI:10.1145/3578339
    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

    Publication History

    Published: 13 March 2023

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    Author Tags

    1. Customer Service
    2. Data Mining Technology
    3. Enterprise Decision-Making
    4. Power Marketing
    5. System Optimization

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