In order to better explore the measurement performance of field service electricity energy meters under multi-dimensional influence conditions

and infer measurement error under standard laboratory conditions

a measurement error estimation method of field service electricity energy meters is proposed based on big data analysis technology

which realizes on-line measurement error estimation by combining environmental data and electric factor data of field operation. Firstly

the K-Means clustering algorithm is improved by optimizing the clustering evaluation index

and the field environmental data is analyzed by clustering. Then

LM algorithm is used to optimize the data training method BP neural network

and modeling relationship between electric energy error and the four factors of environmental temperature

humidity

load current and power factor is constructed. Finally

on the basis of field electric energy error data of electric energy meter

the calculation of engineering application example is carried out. The results show that the proposed method can be used to divide the measurement energy error of electric energy meter

and the accuracy of the proposed method is verified.