Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
This work addresses anomaly detection for energy sector applications, but it is incremental as it adapts an existing metric to a specific domain.
The authors tackled unsupervised anomaly detection by introducing a method based on the sliced-Wasserstein metric, achieving competitive performance on synthetic and standard datasets, and they released the first open dataset for localized critical peak rebate demand response in a northern climate.
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.