  Residential occupancy detection has become an enabling technology in today's
urbanized world for various smart home applications, such as building
automation, energy management, and improved security and comfort.
Digitalization of the energy system provides smart meter data that can be used
for occupancy detection in a non-intrusive manner without causing concerns
regarding privacy and data security. In particular, deep learning techniques
make it possible to infer occupancy from low-resolution smart meter data, such
that the need for accurate occupancy detection with privacy preservation can be
achieved. Our work is thus motivated to develop a privacy-aware and effective
model for residential occupancy detection in contemporary living environments.
Our model aims to leverage the advantages of both recurrent neural networks
(RNNs), which are adept at capturing local temporal dependencies, and
transformers, which are effective at handling global temporal dependencies. Our
designed hybrid transformer-RNN model detects residential occupancy using
hourly smart meter data, achieving an accuracy of nearly 92\% across households
with diverse profiles. We validate the effectiveness of our method using a
publicly accessible dataset and demonstrate its performance by comparing it
with state-of-the-art models, including attention-based occupancy detection
methods.
