Review of electric vehicles' charging data anomaly detection based on deep learning

Abstract

Anomalies in data may be caused by errors in the data, but sometimes the appearance of anomalous data often also suggests the emergence of a new domain or a new underlying process. Anomaly detection has therefore been an active area of research for decades. This paper is based on what anomaly detection is, the main areas of application for anomaly detection, and the challenges of anomaly detection at this stage. Deep learning-based anomaly detection is proposed based on the unpredictability, diversity, and sparsity that exist in anomaly detection, and deep learning anomaly detection algorithms are classified into unsupervised deep anomaly detection algorithms and semi-supervised deep anomaly detection algorithms based on the use of anomaly labels. This paper reviews and analyses the research on these two algorithms, and elaborates on the advantages and disadvantages of each type of anomaly detection algorithm when applied. Deep anomaly detection is also applied to the anomaly detection of charging data and is classified according to the different types of charging data, into deep anomaly detection for electric vehicles, deep anomaly detection for charging stations, and deep anomaly detection for charging poles. Finally, we provide an outlook on various algorithms for anomaly detection and future applications of anomaly detection in charging data.