Explainable Anomaly Detection for Electric Vehicles Charging Stations
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913951272402944 |
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| author | Cederle, Matteo Mazzucco, Andrea Demartini, Andrea Mazza, Eugenio Suriani, Eugenia Vitti, Federico Susto, Gian Antonio |
| author_facet | Cederle, Matteo Mazzucco, Andrea Demartini, Andrea Mazza, Eugenio Suriani, Eugenia Vitti, Federico Susto, Gian Antonio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15718 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable Anomaly Detection for Electric Vehicles Charging Stations Cederle, Matteo Mazzucco, Andrea Demartini, Andrea Mazza, Eugenio Suriani, Eugenia Vitti, Federico Susto, Gian Antonio Machine Learning Artificial Intelligence 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. |
| title | Explainable Anomaly Detection for Electric Vehicles Charging Stations |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.15718 |