LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial Buildings

TL;DR

This paper introduces LEAD1.0, a large-scale annotated dataset of energy consumption in commercial buildings, enabling improved anomaly detection and energy management research.

Contribution

It provides a comprehensive, annotated dataset of over a year of energy data and benchmarks eight anomaly detection methods on this dataset.

Findings

Benchmark results of eight anomaly detection methods

Identification of the most effective anomaly detection techniques

Insights into energy consumption patterns and anomalies

Abstract

Modern buildings are densely equipped with smart energy meters, which periodically generate a massive amount of time-series data yielding few million data points every day. This data can be leveraged to discover the underlying loads, 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 well-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.