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

Power Management in Smart Residential Building with Deep Learning Model for Occupancy Detection by Usage Pattern of Electric Appliances