计量系统下基于边缘计算的电量预测

Electricity Forecasting Based on Edge Computing under Metering System

智能电网计量系统拥有海量电力数据,对数据进行合理高效的数据处理,在接近用户侧充分利用数据进行计量系统的业务改善,可以提高电力效率和用户体验。边缘计算模型将应用服务程序的全部或部分计算任务从云端迁移到网络边缘侧的边缘设备端执行,提高了数据传输效率,保证了数据处理的实时性,同时降低了网络拥塞的可能性。本文提出一种计量系统下的基于边缘计算的电量预测方法,该方法基于边缘计算模型优秀的实时性,结合机器学习领域的梯度提升树算法以及在线学习方式,能够高效并精准地对海量电力数据进行实时训练及进行电量预测。

Smart grid metering system has a large amount of electric power data. It can improve electric power efficiency and user experience by processing data reasonably and efficiently and making full use of data to improve the business of metering system on the user side. The edge computing model migrates all or part of the computing tasks of the application server from the cloud to the edge device on the edge of the network, thus greatly improving data transmission efficiency, and ensuring the real-time performance of data processing, while reducing the possibility of network congestion. In this paper, a method of electricity forecasting based on edge computing under me-tering system is proposed. This method is based on the excellent real-time performance of the edge computing model, combined with the gradient boosting decision tree algorithm and the online learning method in the machine learning field. It can efficiently and accurately perform real-time training and electricity forecasting on massive power data.