Method for repairing missing data in college smart meters
Data loss often occurs in the process of collection,transmission and storage of university operation data.In this regard,this paper proposes a missing data repair method based on improved long short-term memory neural network-chain equation multiple interpolation method.The chain equation multiple interpolation method is used to generate multiple filling values for each missing attribute value through iteration,thereby generating multiple complete data sets,and analyzing and optimizing to obtain a final complete data set.In order to improve the accuracy of missing value repair,in the prediction task of long short-term memory neural network,the sparrow search algorithm is used to optimize the hyperparameters,and the mean matching model is used to repair the missing data.The data of a university in the north of China in 2019 are used for verification.The method proposed in this paper is evaluated by non-natural missing examples and natural missing examples.The results show that the overall attribution error of this method is 0.106 in non-natural missing examples,which is at least 29.3%lower than other models,which verifies the effectiveness of this method.The data under the natural missing rate of 11.8%is filled.The data filled by the method proposed in this paper effectively improves the prediction accuracy of the subsequent operation data of colleges and universities,and indirectly verifies the effectiveness of missing data filling.
university operation datamissing data fillingmultiple interpolation of chain equationLSTM
NETL
NSTL
万方数据
