The conflict between the deployment cost of high-precision intelligent standard instruments and the accuracy of intelligent verification is an unavoidable challenge in the intelligent transformation of charging-pile metering verification. To address this issue, this paper proposes a joint estimation method for charging-pile metering errors based on limited standard data. Specifically, correlated data sample groups are constructed based on the charging records of electric vehicles at different piles, a regularized least-squares convex programming model for joint estimation is established, and the Alternating Least Squares (ALS) algorithm is introduced to solve the model. Simulation results demonstrate that with only 10% of charging piles equipped with standard instruments, the estimation Root Mean Square Error (RMSE) is as low as 0.0069, and the overall identification accuracy reaches 91.25%, whose performance is significantly superior to that of the single-pile independent analysis scheme. Unlike conventional single-pile independent strategies that cannot identify systematic errors or leverage inter-pile data correlation, the proposed method employs an Alternating Least Squares (ALS) algorithm to fuse high-precision standard device data with pile-side reported data, achieving an overall identification accuracy of 91.25%, an F1-score of 72.00%, and an RMSE of 0.0069 for error estimation on a network of 80 charging piles with only 10% standard device coverage—significantly outperforming single-pile independent analysis.
Huang et al. (Wed,) studied this question.