Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2022

Abstract

Modern buildings are densely equipped with smart energy meters, which periodically generate a massive amount of time-series data yielding a few million data points every day. This data can be leveraged to discover the underlying load and infer their energy consumption patterns, inter-dependencies on environmental factors, and the building’s operational properties. Furthermore, it allows us to simultaneously identify anomalies present in the electricity consumption profiles, which is a big step towards saving energy and achieving global sustainability. However, to date, the lack of large-scale annotated energy consumption datasets hinders the ongoing research in anomaly detection. We contribute to this effort by releasing a carefully annotated version of a publicly available ASHRAE Great Energy Predictor III data set containing 1,413 smart electricity meter time series spanning over one year. In addition, we benchmark the performance of eight state-of-the-art anomaly detection methods on our dataset and compare their performance.

Keywords

Smart buildings, smart meters, time-series analysis, outlier detection, anomaly detection, and machine learning

Discipline

Databases and Information Systems

Publication

e-Energy '22: Proceedings of the Thirteenth ACM International Conference on Future Energy Systems, Virtual Conference, June 28 - July 1

First Page

485

Last Page

488

ISBN

9781450393973

Identifier

10.1145/3538637.3539761

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3538637.3539761

Download

Share

COinS