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

• URL: http://arxiv.org/abs/2203.17256v1
• Date: Wed, 30 Mar 2022 07:30:59 GMT
• Title: LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial Buildings
• Authors: Manoj Gulati and Pandarasamy Arjunan
• Abstract summary: We release 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. We benchmark the performance of eight state-of-the-art anomaly detection methods on our dataset and compare their performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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