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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.
Ghosh et al. (Wed,) studied this question.
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