Power user Data Feature Matching Verification Model based on TSVM Semi-supervised Learning Algorithm

The existing model for identifying user data features based on smart meter data adopts a supervised learning method. Although the model has good identification performance under the condition of sufficient index samples, matching data are difficult to obtain and the marking cost is high in real life. The identification accuracy is significantly reduced when the matching data are insufficient or unavailable in the supervised learning method. In view of the above problems, based on the smart meter data, this paper proposes a feature recognition method for residential user data based on semi-supervised learning, which uses three indicators to evaluate the recognition performance of the proposed semi-supervised learning method for residential user data features and to find the appropriate feature selection method and data acquisition resolution. Then, explore the role of this method in real life when there is insufficient or unavailable matching data. Experimental results show that the performance of the proposed semi-supervised learning algorithm is better than that of the supervised learning algorithm, and the accuracy of the proposed algorithm is better than or close to that of the supervised learning algorithm.

@article{Zhu2022,
  title     = {Power user Data Feature Matching Verification Model based on TSVM Semi-supervised Learning Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {8},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Yakui Zhu and Rui Zhang and Xiaoxiao Lu},
  doi       = {10.14569/IJACSA.2022.0130858},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130858}
}