Power Grid Industrial User Prepaid Intelligent Control System Based On Power Quantity Prediction

Although the existing pre-payment system has functionally satisfied the electricity demand of most residential users,its application rate among industrial users is extremely low,because the development of the system focuses on a single payment function,lacks information interaction with users,and lacks a complete electricity charge warning function.As for the existing prepaid intelligent control system,it pays more attention to the optimization of metering statistics and online payment function,but few pay more attention to the improvement of sysrtrm service quality and the lack of information interaction with users.This paper integrates the most commonly used information communication methods into the prepaid intelligent management and control system,and realizes the diversification of system information notification methods through SMS notification,wechat public number notification,voice telephone notification,email notification,etc.In view of many high concurrency scenarios that power systems may face,the existing prepaid systems seldom optimize the data processing function in the case of large data volume.Based on this,the system integrated Redis database and SQL Server database,using the way of data memory to achieve efficient and accurate data processing capabilities.Compared with other prepaid systems,on the basis of optimizing the payment function,this system focuses on improving the power alarm function.CNN-GRU shortterm matching prediction model is adopted to estimate the user’s power in the next few days,accurately judge the use time of the user’s remaining power,accurately realize the power alarm function,and provide convenience for power marketing decision-making.The research shows that the intelligent management and control system developed in this paper has the functions of electricity consumption statistics,online payment,remote control,electricity warning and personnel management.The system uses Redis data processing mode.Compared with the traditional data processing mode,the 90%response time of the system is improved by 60.9%when the data volume is 10KB and the sample size is 10000.In addition,the system can accurately predict the user’s short-term electricity demand to inform the user of electricity information in advance;After the deployment of the system,the electricity recovery rate of the deployed distribution area has increased by 42.86%.The above results also verify the correctness of the system model proposed in the paper,and provide a good solution for power supply enterprises to manage the problem of industrial users’ electricity fee arrears,which has a certain innovation,and can also provide technical and theoretical support for similar work.