Poster
in
Workshop: Tackling Climate Change with Machine Learning
Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
Julien Pallage · Bertrand Scherrer · Salma Naccache · Christophe BĂ©langer · Antoine Lesage-Landry
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Abstract
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Abstract:
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 integration in MLOps pipelines deploying trustworthy machine learning models in critical sectors like energy. 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.
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