Exploration of Energy Meter Data Security and Fast Response Technology in Spot Power Trading

Abstract

With the advancement of smart grid construction, higher requirements have been put forward for energy meter data, which need to realize real-time, accurate, efficient, safe, and economical data transmission. In this paper, an intelligent collection of energy meter data is designed to monitor energy meter data. The PCA algorithm is utilized to downscale the energy meter data, and the LMR algorithm is combined to monitor abnormal data from the meter. Based on the smart contract in blockchain technology, a parallel Pedersen commitment algorithm based on privacy protection is designed, a hybrid signature algorithm is created to ensure the secure transmission of energy meter data and a cluster load balancing model for energy meter data is also designed in combination with Hopfield neural network. The PCA-LMR algorithm identifies 95 anomalies of the data in 12 months and under the encryption of a smart contract. The packet loss rate of energy meter data is maintained at about 2%, and after optimizing the cluster load capacity of energy meter data using the Hopfield neural network, the cluster load variance value is about 1.84 in 20 h. Modern technology can improve the secure transmission of energy meter data and enhance its rapid response ability to ensure the economic benefits of the power system.

Keywords

• pca algorithm
• lmr algorithm
• pedersen commitment algorithm
• hybrid signature
• hopfield neural network
• energy meter data
• 97m50