Explainable Anomaly Detection for Electric Vehicles Charging Stations

TL;DR

This paper presents an unsupervised, explainable anomaly detection method for EV charging stations using Isolation Forest and DIFFI, improving interpretability and root cause analysis in real-world scenarios.

Contribution

It introduces an integrated approach combining Isolation Forest with DIFFI for explainable anomaly detection in EV charging infrastructure, addressing interpretability and root cause identification.

Findings

Effective anomaly detection on real-world data

Enhanced interpretability with DIFFI

Successful identification of key features causing anomalies

Abstract

Electric vehicles (EV) charging stations are one of the critical infrastructures needed to support the transition to renewable-energy-based mobility, but ensuring their reliability and efficiency requires effective anomaly detection to identify irregularities in charging behavior. However, in such a productive scenario, it is also crucial to determine the underlying cause behind the detected anomalies. To achieve this goal, this study investigates unsupervised anomaly detection techniques for EV charging infrastructure, integrating eXplainable Artificial Intelligence techniques to enhance interpretability and uncover root causes of anomalies. Using real-world sensors and charging session data, this work applies Isolation Forest to detect anomalies and employs the Depth-based Isolation Forest Feature Importance (DIFFI) method to identify the most important features contributing to such anomalies. The efficacy of the proposed approach is evaluated in a real industrial case.