基于预测的智能电能表数据简化的轻量级框架

Research on lightweight framework for data simplification of smart meter based on prediction

在高级计量设施(AMI)中,为了最大限度减少智能电能表的数据传输量和数据简化,提出一个基于预测的轻量级框架。首先,建立决策树以找到预测方法和用电数据统计特征之间的关系;然后,分析用电数据的时间序列以提取统计特征;最后,为了增加框架对智能电能表不断变化的数据模式的自适应能力,采用监督式学习方案实时切换到最适合当前数据模式的预测方法。从数据集中提取1年中10个用户的用电数据,每个用户采集的总记录数为17600个。实验结果表明,所提框架可以实现较高的数据简化准确度(DRA)和数据简化率(DRP),其中,DRA最高为96.7%,DRP最高为98.6%。

In advanced metering infrastructure (AMI), in order to minimize data transmission and data simplification of smart meters, a lightweight framework based on prediction is proposed in this paper. Firstly, a decision tree is established to find the relationship between the forecasting method and the statistical characteristics of electricity consumption data. Then, the time series of electricity consumption data is analyzed to extract the statistical characteristics. Finally, in order to increase the adaptive ability of the framework to the changing data modes of smart meters, a supervised learning scheme is adopted to switch to the forecasting method which is most suitable for the current data modes in real time. The electricity consumption data of ten users in one year are extracted from the data set. The total number of records collected by each user is 17 600. The experimental results show that the proposed framework can achieve high data simplification accuracy (DRA) and data simplification rate (DRP), with the highest DRA of 96.7% and DRP of 98.6%.