Optimal Management of Collaborative Goals in Power Enterprises Based on Data Mining Algorithm

Authors

  • Shibo Yang Gansu Tongxing Intelligent Technology Development Co., LTD., Lanzhou 730050, Gansu, China
  • Shaobo Liu State Grid Gansu Electric Power Company Internet Division, Lanzhou 730050, Gansu, China
  • Shuiting Du State Grid Gansu Electric Power Company Information Communication Company, Lanzhou 730050, Gansu, China
  • Zuohu Chen Gansu Tongxing Intelligent Technology Development Co., LTD., Lanzhou 730050, Gansu, China
  • Zhenfen Zhang Gansu Tongxing Intelligent Technology Development Co., LTD., Lanzhou 730050, Gansu, China

Abstract

Data mining, otherwise referred to as discovery of information and knowledge in databases, extracts potentially useful data and expertise from a vast amount of imperfect, sporadic, noisy, fuzzy, and random material that is concealed and hitherto unknown to people. With the continuous development of big data, the power industry and enterprises generate a large amount of data in various forms such as text, video, audio, and images, that indicate enterprise development trends, enable problems to be discovered, and facilitate better decision-making. At present, the operating data of many power companies are fragmented and cannot form a unified business model. Moreover, the efficiency of data retrieval is not high, the storage environment
is not unified, and the utilization rate is low. The stable operation of power enterprises has a great impact on people’s daily life and social production. As reported in this paper, a study was conducted of the optimal management of power enterprises’ collaborative objectives based on data mining algorithms, the current organization of power enterprises was analyzed in depth, and the overall structure of enterprise collaborative management was examined. On this basis, according to the characteristics of multi-objective optimization and large-scale problems, an improved co-evolution method was proposed and used to solve these two types of problems. Research showed that in order to adapt to current developments, the new thermal power production capacity in the February-March period from 2013 to 2019 has been increasing, but new thermal power capacity has been decreasing over the same period since 2020, reaching 5.61 GW in 2022.

Keywords: multi-objective optimization, co-evolutionary algorithms, power companies, data mining

Cite As

S. Yang, S. Liu, S. Du, Z. Chen, Z. Zhang, "Optimal Management of Collaborative Goals in Power Enterprises Based on
Data Mining Algorithm", Engineering Intelligent Systems, vol. 31 no. 2, pp. 165-178, 2023.

 







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Published

2023-03-01

Issue

Vol. 31 No. 2 (2023): International Journal of Engineering Intelligent Systems

Section

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