With the development in information technologies, today's building energy consumption can be well monitored by the building energy management systems. However, in most real applications there is no clear definition of abnormal building energy consumption. To overcome this limitation, this work proposes a novel deep learning based unsupervised anomaly detection framework that includes recurrent neural networks and quantile regression. Moreover, this framework is able to produce a prediction interval to detect and evaluate abnormal building energy consumption. The framework has been applied to analyze the energy data collected from three different residential houses, and anomaly detection results are evaluated by the quantile regression range. The research results can provide promising solutions for building managers to detect abnormal energy performance, and is also valuable to assess the level of anomalies and spot opportunities in energy conservation.

随着信息技术的发展，当今的建筑能耗可以通过建筑能源管理系统很好地监控。但是，在大多数实际应用中，没有明确的建筑能耗异常定义。为了克服这一限制，这项工作提出了一种新颖的基于深度学习的无监督异常检测框架，其中包括递归神经网络和分位数回归。此外，该框架能够产生一个预测间隔，以检测和评估异常的建筑能耗。该框架已应用于分析从三个不同住宅收集的能源数据，并通过分位数回归范围评估异常检测结果。