Recent advances of energy management system in a smart home can lead to load monitoring of electrical appliances for energy saving and reduction of electricity bill. Thus, smart metering technology is getting widely implemented in several electricity distribution networks. Most of the existing research are concentrated towards individual power consumption of different household loads using some machine learning algorithm, which can increase computational burden of the processor. In this article, an improved method for estimation of the individual appliance current is carried out from a group of connected consumer electronics loads. The proposed method consists of two steps, first is to collect and store the current data of individual appliances with varying load for on line application. The second step is to estimate the individual load current using the stored data. In the proposed method, a search-based optimization, i.e., Artificial Bee Colony (ABC) algorithm is used for the estimation of individual electrical load. Suitable simulations and experimental studies are carried out on a practical household system to demonstrate the suitability of the proposed methodology.

中文翻译：

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基于人工蜂群优化的智能家居非侵入式设备负荷监控技术

智能家居中能源管理系统的最新进展可以导致对电器进行负载监控，以节省能源并减少电费。因此，智能计量技术已在多个配电网络中得到广泛实施。现有的大多数研究都集中在使用某种机器学习算法来研究不同家庭负载的个体功耗上，这会增加处理器的计算负担。在本文中，从一组连接的消费电子负载中执行了一种用于估计单个设备电流的改进方法。所提出的方法包括两个步骤，第一步是收集和存储负载变化的单个设备的当前数据，以用于在线应用。第二步是使用存储的数据估算单个负载电流。在提出的方法中，基于搜索的优化，即人工蜂群算法（ABC）用于估计单个电负载。在实际的家用系统上进行了适当的模拟和实验研究，以证明所提出方法的适用性。