# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 5 Conclusions  
In this paper, an innovative method for detecting electricity theft among consumers in a smart grid is presented. Firstly, to address the issue of imbalanced datasets, Time GAN was employed to synthesize data from a limited number of customers engaged in electricity theft, thereby increasing the precision of the detection method. Secondly, a hybrid neural network model was introduced that captured customers' daily, weekly, and monthly energy consumption patterns using Bi-LSTM, ResNet, and AlexNet networks. This approach addressed the limitations of existing methods, which cannot adequately capture the periodicity of electricity consumption or consider the temporal correlations of customers' energy usage based on weeks and months. Finally, the CatBoost classifier was employed to classify customers and achieve both high accuracy and low false detection rates. Comparative and ablation experiments conducted on real electricity consumption data provided by the SGCC demonstrated the efficacy of the proposed method. Notably, the Time GAN method utilized exhibited a 39.53% improvement in accuracy rate, a 53.75% improvement in recall rate, and a 38.59% reduction in false detection rate compared to the original dataset. Similarly, the proposed hybrid multi-time scale neural network model achieved an accuracy rate of 96.64%, a recall rate of 96.87%, and a 3.77% false detection rate, underscoring the effectiveness of the approach.  
The current method may have some limitations, and consideration will be given in future research to the following aspects to address these limitations:  
Data collection issue: The method relies on the energy consumption patterns of customers to detect electricity theft, which requires a large amount of customer electricity data to train the model. If the data collection is insufficient or the data quality is poor, the model may not be able to accurately detect electricity theft. Therefore, further research will be conducted on data pre-processing and augmentation techniques to improve the accuracy and robustness of electricity theft detection.  
Accuracy issue: Although the model uses multiple networks to capture energy consumption patterns at different time scales, its accuracy is still influenced by many factors, such as model parameter settings and network structure. Therefore, more optimization and improvement will be carried out to improve the accuracy of the model.  
Model complexity: The method combines multiple neural network models, making the model complex. This may lead to long training times, high computation costs, and difficulty in explaining the model's decision-making process. Therefore, exploration will be conducted on new model structures to improve the model's performance and generalization ability while maintaining model simplicity in future research.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Precision** & **Recall** & **FPR** & **PR-AUC** & **ROC-AUC** \\ \hline Test 1 & 84.26\% & 85.12\% & 21.93\% & 0.7765 & 0.8434 \\ Test 2 & 86.53\% & 87.06\% & 19.76\% & 0.7974 & 0.8631 \\ Test 3 & 94.25\% & 94.64\% & 9.21\% & 0.8988 & 0.9363 \\ Proposed model & 96.64\% & 96.87\% & 3.77\% & 0.9469 & 0.9684 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of results of model ablation experiments.  
Privacy issue: The method requires access to customer electricity data, which may raise privacy and data security concerns. Therefore, practical applications will require the consideration of privacy protection and data security measures.  
Writing--original draft preparation, Y.S.; writing--review and editing, X.S.; supervision, T.H.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 61961017).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.