论文标题
LEAD1.0：商业建筑中能源异常检测的大规模注释数据集
LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial Buildings
论文作者
论文摘要
现代建筑物密集配备了智能能量计，该建筑物每天定期生成大量的时间序列数据，每天产生几百万个数据点。可以利用这些数据来发现潜在的负载，推断其能耗模式，对环境因素的相互依存以及建筑物的运营特性。此外，它使我们能够同时确定电力消耗概况中存在的异常情况，这是节省能源并实现全球可持续性的重要一步。但是，迄今为止，缺乏大规模注释的能源消耗数据集阻碍了正在进行的异常检测研究。我们通过发布了公开可用的Ashrae伟大能源预测数据集的良好版本，其中包含1,413个智能电表时间序列，该数据集涵盖了一年，我们为这项工作做出了贡献。此外，我们在数据集上基于八种最先进的异常检测方法的性能并比较其性能。
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.
