LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial Buildings
This addresses a data bottleneck for researchers in building energy management, enabling more effective anomaly detection to save energy, though it is incremental as it annotates an existing dataset.
The authors tackled the lack of large-scale annotated datasets for energy anomaly detection in commercial buildings by releasing LEAD1.0, a well-annotated version of the ASHRAE dataset with 1,413 smart meter time series over one year, and benchmarked eight state-of-the-art methods on it.
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.