Dynamic Fusion of Attention Mechanism Imputation Model forMissing Data of Power Load
[1]ZHAO Dong,LI Yarui,WANG Wenxiang,et al.Dynamic Fusion of Attention Mechanism Imputation Model forMissing Data of Power Load[J].Journal of Zhengzhou University (Engineering Science),2024,45(pre):1-8.[doi:10.13705/j.issn.1671-6833.2024.05.004]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
45
Number of periods:
2024 pre
Page number:
1-8
Column:
Public date:
2024-12-31
- Title:
- Dynamic Fusion of Attention Mechanism Imputation Model forMissing Data of Power Load
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2024.05.004
- Abstract:
- In order to improve the accuracy of missing value filling of power load data and ensure the efficient follow-up data analysis and application, a filling model based on dynamic fusion attention mechanism is proposed. The model consists of an attention mechanism module and a dynamic weighted fusion module, and the deep association between features and timestamps is mined through two different attention mechanisms of the attention mechanism module. The learnable weights are assigned to the two outputs of the attention mechanism module by the dynamic weighted fusion module to get the feature representation. Finally, the feature representation is used to replace the values at the missing positions to obtain accurate filling results. The proposed model is validated using the meteorological and load dataset of a certain area of New York City and the UCI power load dataset, and the experimental results show that DFAIM has certain advantages over statistical, machine learning, and deep learning filling models in MAE, RMSE, and MRE.
- References:
- -
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Last Update:
2024-05-23