Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912138413473792 |
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| author | Pallage, Julien Scherrer, Bertrand Naccache, Salma Bélanger, Christophe Lesage-Landry, Antoine |
| author_facet | Pallage, Julien Scherrer, Bertrand Naccache, Salma Bélanger, Christophe Lesage-Landry, Antoine |
| contents | In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e.g., energy, as it offers a conservative data selection. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21712 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates Pallage, Julien Scherrer, Bertrand Naccache, Salma Bélanger, Christophe Lesage-Landry, Antoine Machine Learning In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e.g., energy, as it offers a conservative data selection. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset. |
| title | Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.21712 |