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.

本文提出了一个利用智能电表数据进行建筑物占用检测的深度学习模型ABODE-Net，并采用了一种称为Parallel Attention的新型并行关注机制，结合时间、变量和通道注意力实现对重要特征的识别。在两个广泛应用于建筑物占用检测的智能电表数据集上，模型表现优于浅层机器学习和深度学习模型，有效地实现了非侵入式建筑物占用检测。

ABODE-Net: 一种基于注意力机制的深度学习模型，利用智能电表数据进行非侵入式建筑物占用检测