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

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