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.

中文翻译：

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基于深度学习模型的智能住宅建筑中的电源管理，用于根据电器使用模式进行占用检测

随着智能建筑应用的增长，住宅建筑的占用信息变得越来越重要。在智能建筑范式的背景下，这类信息需要用于广泛的目的，包括提高能源效率和居住者的舒适度。在本研究中，基于电器技术信息的深度学习实现了住宅楼的占用检测。为此，提出了一种用于智能住宅建筑系统的占用检测新方法。使用智能计量系统测量的50户家庭的电器、传感器、灯光和暖通空调数据集进行模拟。为了对数据集之间的占用进行分类，使用了支持向量机和自动编码器算法。混淆矩阵用于准确度、精确度、召回率和 F1，以展示所提出的方法在占用检测中的比较性能。所提算法利用电器技术信息实现了占空检测95.7~98.4%。为了验证占用检测数据，采用了主成分分析和 t 分布随机邻域嵌入 (t-SNE) 算法。通过使用占用检测，可再生能源系统的功耗在智能建筑中降低到 11.1~13.1%。采用主成分分析和 t 分布随机邻域嵌入 (t-SNE) 算法。通过使用占用检测，可再生能源系统的功耗在智能建筑中降低到 11.1~13.1%。采用主成分分析和 t 分布随机邻域嵌入 (t-SNE) 算法。通过使用占用检测，可再生能源系统的功耗在智能建筑中降低到 11.1~13.1%。