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

• URL: http://arxiv.org/abs/2212.11396v1
• Date: Wed, 21 Dec 2022 22:37:42 GMT
• Title: ABODE-Net: An Attention-based Deep Learning Model for Non-intrusive Building Occupancy Detection Using Smart Meter Data
• Authors: Zhirui Luo, Ruobin Qi, Qingqing Li, Jun Zheng, Sihua Shao
• Abstract summary: Occupancy information is useful for efficient energy management in the building sector. Massive high-resolution electrical power consumption data collected by smart meters make it possible to infer buildings' occupancy status in a non-intrusive way. 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.
• Score: 5.732496048593952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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