Evaluation on power information data asset management system based on BP neural network

Abstract

With the popularization and development of the current power system, power information data asset management plays a crucial role in modern power systems. However, traditional management methods have some problems, such as low information processing efficiency, low prediction accuracy, and insufficient decision support. In order to better promote its development and achieve efficient management of power information data assets, this article aimed to use BP neural network (Back Propagation Neural Network) to design a power information data asset management system, achieving efficient processing and accurate analysis of power information data. In the article, data preprocessing was achieved through data separation, data cleaning, and data normalization processing. Compared with the traditional power asset management system, it has better management efficiency, lightens the difficulty of asset management and reduces the error rate. In this paper, the power information data is modeled and trained by BP neural network modeling, and the performance index is minimized by error back propagation, and the optimized BP neural network model is integrated into the power information data asset management system to realize data processing and decision support. In order to verify the performance of the power information data asset management system based on BP neural network, this paper tested its system performance. The research results showed that the average processing accuracy of the system under this method for basic data in 10 test cases reached 91.467 %, and the average rationality of decision support reached 89.6 %. The average processing accuracy of real-time data reached 91.625 %, and the average rationality of decision support reached 90.25 %. The average processing accuracy of application data reached 90.675 %, and the average rationality of decision support reached 90.2 %. The results showed that the system under this method has higher accuracy in data processing and can better achieve decision support. This study highlighted the important impact of BP neural networks on data processing, data prediction, decision support, and data security in power information data asset management systems, providing more possibilities for achieving efficient processing and accurate analysis of power information data.