Proceedings of the 2023 8th International Conference on Engineering Management (ICEM 2023)

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Research on Multi-scenario Application of Power Data Mining for Digital Government

Authors
Kaiming Qin1, Peiyuan Guo1, Ding Han1, Liqian Zhang2, *
1Economic and Technological Research Institute of State Grid Henan Electric Power Company, Zhengzhou, Henan, China
2Xi’an International Studies University, Xi’an, China
*Corresponding author. Email: 18209460659@163.com
Corresponding Author
Liqian Zhang
Available Online 11 December 2023.
DOI
10.2991/978-94-6463-308-5_21How to use a DOI?
Keywords
electricity data; digital government; datamining; multi-scenario applications
Abstract

The construction of digital government is a new trend to comply with the digital transformation of the economy and society, and is also a new way and a new way to promote the modernization of the nation governance system and governance capacity. As a production factor, the data information of the electric power system contains a large amount of transformation value, this paper starts from the theoretical perspective of electric power data, analyzes the demand of the main body of the construction of digital government and the status quo of electric power data application capacity, summarizes the overall demand of digital government for electric power data mining, elaborates on the five application scenarios of electric power datamining to support the digital government, and demonstrates that electric power data mining can support the role of the digital government from the industrial chain balance and aggregation application cases. data mining to support the digital government. Through the case study, it can be seen that electric power data mining can provide decision-making basis for many fields of digital government construction.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 8th International Conference on Engineering Management (ICEM 2023)
Series
Atlantis Highlights in Engineering
Publication Date
11 December 2023
ISBN
978-94-6463-308-5
ISSN
2589-4943
DOI
10.2991/978-94-6463-308-5_21How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Kaiming Qin
AU  - Peiyuan Guo
AU  - Ding Han
AU  - Liqian Zhang
PY  - 2023
DA  - 2023/12/11
TI  - Research on Multi-scenario Application of Power Data Mining for Digital Government
BT  - Proceedings of the 2023 8th International Conference on Engineering Management (ICEM 2023)
PB  - Atlantis Press
SP  - 196
EP  - 206
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-308-5_21
DO  - 10.2991/978-94-6463-308-5_21
ID  - Qin2023
ER  -