A Missing Data Compensation Method Using LSTM Estimates and Weights in AMI System

Information

Pan-Koo Kim¹  Hyuk-Rok Kwon² 

[1] Department of Computer Engineering, Chosun University, 309, Pilmun-daero, Dong-gu, Gwangju 61452, Korea;Kepco Kdn Co., Ltd., 661, Bitgaram-ro, Naju-si 58322, Korea;

关键词: AMI;    smart meter;    VEE (validation estimation editing);    missing data;    estimation;    weighted;   

DOI  :  10.3390/info12090341

来源: DOAJ

【 摘 要 】

With the expansion of advanced metering infrastructure (AMI) installations, various additional services using AMI data have emerged. However, some data is lost in the communication process of data collection. Hence, to address this challenge, the estimation of the missing data is required. To estimate the missing values in the time-series data generated from smart meters, we investigated four methods, ranging from a conventional method to an estimation method applying long short-term memory (LSTM), which exhibits excellent performance in the time-series field, and provided the performance comparison data. Furthermore, because power usages represent estimates of data that are missing some values in the middle, rather than regular time-series estimation data, the simple estimation may lead to an error where the estimated accumulated power usage in the missing data is larger than the real accumulated power usage appearing in the data after the end of the missing data interval. Therefore, this study proposes a hybrid method that combines the advantages of the linear interpolation method and the LSTM estimation-based compensation method, rather than those of conventional methods adopted in the time-series field. The performance of the proposed method is more stable and better than that of other methods.

【 授权许可】

Unknown