Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates

TL;DR

This paper introduces a novel unsupervised anomaly detection method based on the sliced-Wasserstein metric, suitable for critical sectors like energy, and provides an open dataset for localized demand response analysis.

Contribution

It presents a new sliced-Wasserstein-based anomaly detection technique and releases the first dataset for localized critical peak rebate demand response in a northern climate.

Findings

Effective on synthetic and standard datasets

Establishes a benchmark for localized demand response data

Demonstrates utility in MLOps pipelines for critical sectors

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.