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

arXiv: https://arxiv.org/abs/2203.17256 · HTML: https://mlsanity.com/paper/2203.17256/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network](https://mlsanity.com/paper/1703.09752/) (2017)
- [The ASHRAE Great Energy Predictor III competition: Overview and results](https://mlsanity.com/paper/2007.06933/) (2020)

## Built on by (most cited first, 3 of 3)

- [Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection](https://mlsanity.com/paper/2402.14384/) (2024)
- [An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework](https://mlsanity.com/paper/2501.00107/) (2024)
- [Validated Data Onboarding for AI Demand Forecasting on U.S. Building Meter Data: Design, Controlled Evaluation, and a Corrected Negative Result](https://mlsanity.com/paper/2610.02397/) (2026)

## Abstract (arXiv)

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.