Leveraging data collected from smart meters in buildings can aid in developing policies towards energy conservation. Significant energy savings could be realised if deviations in the building operating conditions are detected early, and appropriate measures are taken. Towards this end, machine learning techniques can be used to automate the discovery of these abnormal patterns in the collected data. Current methods in anomaly detection rely on an underlying model to capture the usual or acceptable operating behaviour. In this paper, we propose a novel attention mechanism to model the consumption behaviour of a building and demonstrate the effectiveness of the model in capturing the relations using sample case studies. A real-world dataset is modelled using the proposed architecture, and the results are presented. A visualisation approach towards understanding the relations captured by the model is also presented.

翻译：利用从建筑智能电表收集的数据，有助于制定节能政策。若能及早检测到建筑运行条件的偏差并采取适当措施，便可实现显著的能源节约。为此，机器学习技术可用于自动发现收集数据中的异常模式。当前的异常检测方法依赖于潜在模型来捕捉正常或可接受的运行行为。本文提出了一种新颖的注意力机制来建模建筑的能耗行为，并通过案例研究展示了该模型在捕捉相关关系方面的有效性。采用所提出的架构对真实世界数据集进行建模，并展示了结果。此外，还呈现了一种可视化方法，用于理解模型所捕捉到的关系。