Occupancy information is useful for efficient energy management in the building sector. The massive high-resolution electrical power consumption data collected by smart meters in the advanced metering infrastructure (AMI) network make it possible to infer buildings' occupancy status in a non-intrusive way. In this paper, we propose a deep leaning model called ABODE-Net which employs a novel Parallel Attention (PA) block for building occupancy detection using smart meter data. The PA block combines the temporal, variable, and channel attention modules in a parallel way to signify important features for occupancy detection. We adopt two smart meter datasets widely used for building occupancy detection in our performance evaluation. A set of state-of-the-art shallow machine learning and deep learning models are included for performance comparison. The results show that ABODE-Net significantly outperforms other models in all experimental cases, which proves its validity as a solution for non-intrusive building occupancy detection.
翻译:在建筑部门,占用信息对于高效的能源管理是有用的。在先进的计量基础设施(AMI)网络中,智能米收集的大规模高分辨率电能消耗数据使得有可能以非侵入方式推断建筑物的占用状况。在本文中,我们提议了一个称为ABODE-Net的深度倾斜模型,该模型使用一个新型平行关注(PA)块,用于使用智能计量数据进行建筑物占用探测。PA块将时间、变量和引力模块结合起来,同时表示占用探测的重要特征。我们在绩效评估中采用了两个用于建造占用探测的智能计量数据集。一套最先进的浅层机器学习和深层学习模型用于业绩比较。结果显示,ABODE-Net在所有实验案例中都明显优于其他模型,这证明它作为非侵入性建筑占用探测的解决方案是有效的。