About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
ew 24(1):

Editorial

Risk prediction method for power Internet of Things operation based on ensemble learning

Download63 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/ew.6045,
        author={Chao Hong and Xiaoyun Kuang and Yiwei Yang and Yixin Jiang and Yunan Zhang},
        title={Risk prediction method for power Internet of Things operation based on ensemble learning},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={12},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2025},
        month={4},
        keywords={adaptive synthetic oversampling, ensemble learning, power internet of things, prediction, risk},
        doi={10.4108/ew.6045}
    }
    
  • Chao Hong
    Xiaoyun Kuang
    Yiwei Yang
    Yixin Jiang
    Yunan Zhang
    Year: 2025
    Risk prediction method for power Internet of Things operation based on ensemble learning
    EW
    EAI
    DOI: 10.4108/ew.6045
Chao Hong1,*, Xiaoyun Kuang1, Yiwei Yang, Yixin Jiang1, Yunan Zhang1
  • 1: CSG Electric Power Research Institute Co.
*Contact email: hongchao_2024@163.com

Abstract

INTRODUCTION: The power Internet of Things is an important strategic support for the State Grid Corporation of China to build an international leading energy internet enterprise. However, the operating environment of the power Internet of Things is complex and varied, which has serious implications for the safe operation of the power Internet of Things. OBJECTIVES: To timely predict the various risk. METHODS: A data set is fused based on time series. The training set is over-sampled using an adaptive synthetic oversampling method. Then, by jointly considering the contribution of features to classification and the correlation between features, a risk prediction method ground on ensemble learning is established. RESULTS: From the results, the accuracy of predicting 5 risk categories increased by 7.00%, 1.10%, 2.20%, 2.30%, and 0.60%, respectively, reducing the features from the original 118 columns to 60 columns and reducing the data dimension by 49.00%. Compared with traditional models, the accuracy was 98.61%, and the overall accuracy was improved by 0.60%. CONCLUSION: This risk prediction scheme can quickly and accurately predict the risk categories that affect its operation. It has high prediction accuracy and fast speed than other algorithms. This research can provide strong assistance for security decision-making in the power Internet of Things.

Keywords
adaptive synthetic oversampling, ensemble learning, power internet of things, prediction, risk
Received
2025-04-11
Accepted
2025-04-11
Published
2025-04-11
Publisher
EAI
http://dx.doi.org/10.4108/ew.6045

Copyright © 2025 Ch. Hong et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL