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Occupancy information is useful for efficient energy management in thebuilding sector. The massive high-resolution electrical power consumption datacollected by smart meters in the advanced metering infrastructure (AMI) networkmake 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 anovel Parallel Attention (PA) block for building occupancy detection usingsmart meter data. The PA block combines the temporal, variable, and channelattention modules in a parallel way to signify important features for occupancydetection. We adopt two smart meter datasets widely used for building occupancydetection in our performance evaluation. A set of state-of-the-art shallowmachine learning and deep learning models are included for performancecomparison. The results show that ABODE-Net significantly outperforms othermodels in all experimental cases, which proves its validity as a solution fornon-intrusive building occupancy detection.

ABODE-Net: An Attention-based Deep Learning Model for Non-intrusive Building Occupancy Detection Using Smart Meter Data