  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.
