# Risk Assessment Method of Solar Smart Grid Network Security Based on TimesNet Model
## 5 Conclusions  
To provide a reliable basis for SSG-NSR evaluation, this study constructed a TimesNet-based SGTBD model and a Bi-LSTM-based SGID model. In the experiment, as the window for electricity theft detection increased, the DR of the SGTBD model gradually increased and the FDR gradually decreased. When the proportion of electricity theft data was 25%, the FDR of the model was 3.52. The accuracy and recall of the complete model were the highest, at 97.15% and 96.45%. The accuracy and recall of the traditional TimesNet model were the lowest, at 90.47% and 89.43%. The AUC value of the proposed model was outperformed that of the comparative model, which was 0.98. The model performance in terms of DR, FDR, F1 value, and accuracy was superior to the comparative model, with  
Figure 11: Comparison of intrusion detection performance among four models. Figure 12: Comparison of accuracy among four models. scores of 97.04%, 1.21%, 92.69%, and 97.15%, respectively. The detection accuracy of the proposed SGID model was the highest, at 97.54%, followed by the Bi-LSTM (91.43%), and the DBN was the lowest (90.07%). The FNR of the proposed model was the lowest, at 1.21%. The FNR of DBN was the highest, at 2.48%. Traditional methods for detecting electricity theft include statistical analysis, machine learning models, and time series models. Among them, statistical analysis methods usually rely on manual experience and are difficult to capture complex time series patterns, resulting in a high false alarm rate. Machine learning models such as support vector machines and random forests are unable to effectively model long-term dependencies and nonlinear time relationships. LSTM and other time series models have poor adaptability to complex electricity consumption patterns. The smart grid electricity theft detection model based on TimesNet proposed by the research institute extracts time series features through Tsfresh and uses MIC algorithm for feature screening, which can better capture complex patterns and correlations in the data, thereby improving the performance of electricity theft detection. In summary, both the proposed SGTBD and SGID models have good detection performance, and their application in SSG-NSR evaluation can help improve the accuracy of the evaluation. In actual production environments, the data distribution of smart grids may differ from the training data of the model, which may affect the generalization ability of the model. And the model may face a lot of data cleaning and preprocessing work during implementation, and the diversity and complexity of data may have a certain impact on the performance of the model. To ensure the practical application effect of the proposed model, it is necessary to strengthen the cleaning and processing of data. However, while the proposed intrusion detection model improves detection performance, it also has high complexity and requires more training time. Therefore, future research should further explore lightweight power grid intrusion detection methods to improve the efficiency of SSG-NSR evaluation. Conceptualization, Y.C. and B.Z. ; methodology, Y.C. ; software, B.Z. ; resources, B.Z. ; data curation, B.Z. ; writing--original draft preparation, Y.C. ; writing--review and editing, B.Z. ; project administration, Y.C. ; funding acquisition, B.Z. All authors have read and agreed to the published version of the manuscript. This work was supported by National Natural Science Foundation of China Youth Project (52307133). * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] Smart grid security: Attacks and defence techniques. _IET Smart Grid_**2023**, \(6\), 103-123. [CrossRef]
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