Interpolation Method of Missing Residential Meter Data Based on Similar Day and BGCP

Residential electricity data in the collection, transmission and other aspects of the irregular missing will lead to errors in the subsequent application of the analysis, in order to ensure the integrity of the residential electricity data, for the current data missing because of smart meter sensors are subject to interference and other issues, research a similar day selection based on similar day Bayesian Gaussian tensor decomposition model of residential electricity data interpolation method. The method selects the meter data adjacent to the missing data meters in space, and in time, comprehensively considers the time series data and the similar day series data, so as to construct the spatio-temporal tensor model, fully exploits the intrinsic relationship existing between each data, and utilizes the Bayesian Gaussian CP decomposition (BGCP) model to The missing data are recovered to achieve the purpose of data restoration and improve the integrity and accuracy of the grid data. The experimental results show that: the proposed method can effectively interpolation the missing data of residential electricity consumption, and the error rate after data interpolation is below 0.4%, which can meet the needs of data application analysis.