Research on Power Plant Production Data Mining Technology Based on Association Rules

Chao Wen, Hao Zhang, Zengtao Zhao, Tao Peng, Zihang Liang

2025

Abstract

In the production process, the power plant will generate a huge and important data asset, and if the knowledge and rules can be effectively mined and utilized, it will certainly provide important support for the operation efficiency and production cost reduction of the power plant. This paper systematically expounds the research on power plant production excavation technology based on association rules and makes a detailed study of each link in it. Based on the combination of theoretical analysis, qualitative analysis and case verification methods, it can be found that this technology can effectively explore the value rules existing in the production data of power plants and provide certain support for the intelligent management of power plants. The research results of this paper can provide a strong reference for the digital transformation and practice of electric power.

Paper Citation

in Harvard Style

Zhang H., Wen C., Zhao Z., Peng T. and Liang Z. (2025). Research on Power Plant Production Data Mining Technology Based on Association Rules. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 447-452. DOI: 10.5220/0013545300004664

in Bibtex Style

@conference{incoft25,
author={Hao Zhang and Chao Wen and Zengtao Zhao and Tao Peng and Zihang Liang},
title={Research on Power Plant Production Data Mining Technology Based on Association Rules},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={447-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013545300004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Research on Power Plant Production Data Mining Technology Based on Association Rules
SN - 978-989-758-763-4
AU - Zhang H.
AU - Wen C.
AU - Zhao Z.
AU - Peng T.
AU - Liang Z.
PY - 2025
SP - 447
EP - 452
DO - 10.5220/0013545300004664
PB - SciTePress