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A Hybrid Anomaly Detection Method for Electric Vehicle Identification from Smart Meter Recordings — arXiv:2611.01655

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01655
License (as archived): CC0-1.0
Archived: 2026-11-03T10:27:43.437Z
SHA-256 of the archived text: 254c46fd5092003fa7c117ba4db4ee1492aaa204053e18a53b883ab3ca3f6fd2
Stable URL of this page: https://metraipolis.com/documento/dc_3bf796d6fbfa

How to cite

A Hybrid Anomaly Detection Method for Electric Vehicle Identification from Smart Meter Recordings — arXiv:2611.01655 — arXiv (en), https://arxiv.org/abs/2611.01655 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3bf796d6fbfa (Metraipolis archive dc_3bf796d6fbfa, sha256 254c46fd5092003fa7c117ba4db4ee1492aaa204053e18a53b883ab3ca3f6fd2)

BibTeX

@misc{dc3bf796d6fbfa,
  title = {A Hybrid Anomaly Detection Method for Electric Vehicle Identification from Smart Meter Recordings — arXiv:2611.01655},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01655},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3bf796d6fbfa; sha256 254c46fd5092003fa7c117ba4db4ee1492aaa204053e18a53b883ab3ca3f6fd2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01655. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In recent years, electric vehicles (EVs) have attracted much attention due to their potential to decarbonize the transport system. EV charging imposes challenges on power grids, such as load imbalance and voltage instability. Therefore, electricity distributors need to identify which users have an EV. Smart meter recordings can be effectively used for this purpose. The major issue associated with such data is the imbalance problem, where the majority of the data belongs to users without EVs. The existing methods for EV identification from smart meter recordings have framed it as a classification task. In this paper, we frame the problem as an unsupervised problem and propose a hybrid anomaly detection method that combines the power of prediction-based and reconstruction-based anomaly detection methods. The proposed model considers temporal and stochastic representation information of smart meter time series via prediction-based and reconstruction-based components, respectively. The performance of the proposed model is compared with state-of-the-art supervised and unsupervised methods. Its superiority is verified on real-world data.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.01655 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:19:50.107Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).