Power Data Identification of Smart Grid: Application of Multi-source Semantic Analysis

As the basis of smart grid operation, power data contains rich system operation status information and user behavior characteristics. Traditional power data identification methods have limitations when dealing with multi-source, heterogeneous, massive and time-sensitive power data. Aiming at the challenge of power data identification in smart grid, this paper explores and proposes a technical scheme based on multi-source semantic analysis, constructs a power data identification model based on multi-source semantic analysis, and comprehensively evaluates its performance through experiments. The experimental results show that, compared with the traditional methods, the model proposed in this paper has significantly improved the key indicators such as prediction accuracy, real-time, F1 score and mean absolute error (MAE), with the prediction accuracy increased by about $15 \%$, the prediction time reduced by about $30 \%$, the $F 1$ score increased by about $\mathbf{2 0 \%}$ and the MAE decreased by about $\mathbf{2 5 \%}$. These improvements fully verify the effectiveness of multi-source semantic analysis in power data identification. Finally, this paper summarizes the advantages of the model, and points out the existing problems and the future improvement direction, in order to provide reference for promoting the application of power data identification technology.