# Risk Assessment Method of Solar Smart Grid Network Security Based on TimesNet Model
## 2 Related Works  
Smart grid security not only involves equipment security at the physical level but also includes data security and system stability at the information level. [PERSON] et al. discussed the impact of inter-regional oscillations on system safety in response to the significant decrease in system inertia caused by the increase in wind and solar power generation devices. They proposed a location-based frequency change rate safety constrained unit model. This model could effectively enhance the market efficiency of the power grid model [6]. [PERSON] et al. classified network resilience methods that can protect smart grid systems from intentional attacks, targeting the problem of smart grid network attacks caused by the use of heterogeneous communication technologies. This study emphasized the most critical challenge in achieving network resilience, which could help improve the network resilience of smart grids [7]. [PERSON] et al. stated that protection and control systems greatly improve the reliability of smart grid systems, but also increase the risk of potential attack surfaces and critical vulnerabilities. Therefore, they provided a key classification of the scope of network security, guiding the investigation of research in smart grid security [8]. [PERSON] et al. proposed a privacy protected authentication scheme for demand response management to resist various attacks and conducted security analysis to address the security issues encountered in the deployment of smart meters and wireless communication in smart grids. The proposed method provided high-level security and had certain practical application value [9]. [PERSON] et al. developed an optimization strategy built on NDSGA-II for fault detection in smart grids, aiming to keep the security of the power grid at the lowest cost. This method had advantages in optimizing maintenance completion and the distribution of non-dominated solution sets, and could effectively maintain the security of smart grids [10]. [PERSON] et al. summarized the latest research results on quantum computing in response to the high computational complexity of smart grid systems, and further identified potential smart grid applications that can overcome computational limitations [11]. Electricity theft detection refers to the process of identifying and confirming whether users engage in illegal electricity use through a series of technical means and inspection procedures. The modern electricity theft detection models mainly include deep learning-basedmodels and data mining-based models. [PERSON] et al. designed a homomorphic encryption friendly model based on region search and clustering, as well as a theft detection model that uses neural networks to infer encrypted data, to address privacy issues in the process of electricity theft detection. The results indicate that the AUC value of the proposed model is 0.763 [12]. [PERSON] et al. proposed a hybrid gate convolutional autoencoder model based on skip group algorithm to address the complex hyperparameter adjustment process in machine learning based electricity theft detection methods. The results indicate that the proposed model has a theft detection accuracy of 99.45% [13]. [PERSON] et al. proposed a theft detection model based on graph convolutional neural network and convolutional neural network to address the problem that most existing theft detection models cannot capture temporal dependencies. The results indicate that the proposed model outperforms popular benchmarks in electricity theft detection [14]. [PERSON] et al. proposed a privacy protection protocol based on threshold homomorphic encryption and a decentralized federated learning framework to address privacy issues in the process of electricity theft detection. The results indicate that the proposed models have similar performance to centralized models [15]. The TimesNet model is a deep learning model used for time series analysis.