Abstract
In the context of the increasing spread of electric vehicle (EV) charging stations, the accuracy and reliability of electric energy measurement is becoming increasingly important for consumers. Degradation in the performance of smart meters at these stations is often due to factors such as aging and malfunctions. Traditional approaches to solving this problem usually involve manual on-site inspections, which require significant investment in manpower and materials. To overcome this challenge, this study proposes an error estimation method that integrates highway convolutional neural networks with bidirectional long short-term memory (LSTM) networks, which enables real-time prediction of measurement performance at charging piles. First, the convolutional module is combined with the highway network to extract spatial features from smart meter data for charging facilities while retaining some original information to improve model prediction performance. The features are then fed into a bidirectional LSTM network to obtain temporal characteristics, which improves the accuracy of relative error predictions. Empirical validation of this method at a charging station in the region has shown that it has higher efficiency compared to existing advanced models.