Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates (Papers Track)
Julien Pallage (Polytechnique Montréal, Mila, GERAD); Bertrand Scherrer (Hydro-Québec); Salma Naccache (Hydro-Québec); Christophe Bélanger (Hydro-Québec); Antoine Lesage-Landry (Polytechnique Montréal & GERAD)
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 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.