The metering error prediction method for charging pile based on knowledge-assisted modal decomposition

As a supporting device for electric vehicles, DC charging piles are widely distributed and in large quantities, involving a huge emerging electricity trading market. Ensuring metering accuracy of charging pile is critical to maintaining fair electricity trading. The traditional on-site verification method for charging pile involves high personnel input and low verification efficiency, making it difficult to meet the massive metering verification demand. In this paper, based on knowledge-assisted modal decomposition, the metering error prediction method for charging pile is proposed to remotely locate the charging pile whose metering error is about to exceed the threshold in advance. First, the trend and multi-period characteristics of metering error data—driven by factors such as temperature, humidity, electrical stresses, and user behavior—are analyzed. With an adaptive data imputation method, high-ratio continuous missing values in metering data time series are completed. Then, the error data time series is decomposed into trend, multi-level periodic, and residual terms with the improved seasonal-trend decomposition method. Finally, the trend and multiple periodic terms are predicted based on the support vector regression model, and they are combined to form the error prediction. The effectiveness and superiority of the proposed method are validated through practical application.