School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea.
Sensors (Basel). 2020 Dec 15;20(24):7187. doi: 10.3390/s20247187.
Home Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the electrical energy demands, scheduling the operational time of appliances, applying machine learning models in real-time, optimal human appliances interaction, etc. In order to address the aforementioned challenges and control the wastage of energy due to the lifestyle of the home users, we propose a system for automatically controlling the energy consumption by employing machine and deep learning techniques to smart home networks. The proposed system works in three phases, (1) feature extraction and classification based on 1-dimensional Deep Convolutional Neural Network (1D-DCNN) which extract important energy patterns from the historic energy data, (2) a load forecasting system based on Long-short Term Memory (LSTM) is proposed to forecast the load based on the extracted features in phase 1 and (3) a scheduling algorithm based on the forecasted data obtained from phase 2 is designed to schedule the operational time of smart home appliances. The proposed scheme efficiently automates the smart home appliances to consume less energy while adapting to the lifestyle of smart home users. The validation of the proposed scheme is tested with a number of simulation scenarios incorporating datasets from authentic data sources. The simulation results show that the proposed smart home automation system can be a game-changer in fulfilling the energy demands of the home users without installing renewable and other energy sources in the future.
智能家居自动化系统（HAS）在过去十年中受到了广泛关注，这得益于新无线技术的发展，如蓝牙 4.0、5G、Wi-Fi 6 等。为了实现智能家居中的自动化服务，需要解决许多挑战，如满足电能需求、安排电器的运行时间、实时应用机器学习模型、实现人与电器的最佳交互等。为了解决上述挑战并控制由于家庭用户生活方式而导致的能源浪费，我们提出了一种通过采用机器和深度学习技术来控制智能家居网络能源消耗的系统。该系统分三个阶段工作：（1）基于一维深度卷积神经网络（1D-DCNN）的特征提取和分类，从历史能源数据中提取重要的能源模式；（2）提出了基于长短期记忆（LSTM）的负荷预测系统，根据第 1 阶段提取的特征预测负荷；（3）基于第 2 阶段获得的预测数据设计调度算法，以安排智能家居电器的运行时间。该方案通过自动控制智能家居电器来提高能源效率，同时适应智能家居用户的生活方式。通过结合来自真实数据源的数据集的多个模拟场景对所提出的方案进行了验证。仿真结果表明，所提出的智能家居自动化系统可以在未来无需安装可再生能源和其他能源的情况下，满足家庭用户的能源需求，从而改变游戏规则。
