Risk prediction method for power Internet of Things operation based on ensemble learning
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
European Alliance for Innovation (EAI)
2025-02-01
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| Series: | EAI Endorsed Transactions on Energy Web |
| Subjects: | |
| Online Access: | https://publications.eai.eu/index.php/ew/article/view/6045 |
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| Summary: | 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.
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| ISSN: | 2032-944X |