Power management in smart residential building with deep learning model for occupancy detection by usage pattern of electric appliances

Abstract

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 based on technical information of electric appliances is implemented using deep learning. The dataset of electric appliances, sensors, light, and HVAC, that is measured by smart metering system and collected from 50 households is used for simulations. To classify the occupancy among datasets, support vector machine and autoencoder algorithm are used. The proposed autoencoder uses the GCN-GRU layers. 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.