Method of estimation of missing data in AMI system.

As AMI installation is expanded, various additional services using AMI data are emerging. However, data is missing in the communication process of collecting data. Estimation missing data is necessary to solve these problems. In order to estimate for missing values of time series data measured from smart meters, a total of four methods were experimented and the performance comparison data were provided, from traditional methods to the estimation method applied with good LSTM in the field of time series. In addition, since power usage is not a typical time series prediction data, but rather estimation of data that results in an intermediate missing, a simple prediction can cause errors that reverse the data that appear after the missing. For this reason, the linear interpolation method was proved to be stable and better performing than the general time series field prediction estimation method.