  With the growth of smart building applications, occupancy information in
residential buildings is becoming more and more significant. In the context of
the smart buildings' paradigm, this kind of information is required for a wide
range of purposes, including enhancing energy efficiency and occupant comfort.
In this study, occupancy detection in residential building is implemented using
deep learning based on technical information of electric appliances. To this
end, a novel approach of occupancy detection for smart residential building
system is proposed. The dataset of electric appliances, sensors, light, and
HVAC, which is measured by smart metering system and is collected from 50
households, is used for simulations. To classify the occupancy among datasets,
the support vector machine and autoencoder algorithm are used. Confusion matrix
is utilized for accuracy, precision, recall, and F1 to demonstrate the
comparative performance of the proposed method in occupancy detection. The
proposed algorithm achieves occupancy detection using technical information of
electric appliances by 95.7~98.4%. To validate occupancy detection data,
principal component analysis and the t-distributed stochastic neighbor
embedding (t-SNE) algorithm are employed. Power consumption with renewable
energy system is reduced to 11.1~13.1% in smart buildings by using occupancy
detection.
