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A microgrid energy management system based on non-intrusive load monitoring via multitask learning

Abstract:

Non-intrusive load monitoring (NILM) enables to understand the appliance-level behavior of the consumers by using only smart meter data, and it mitigates the requirements such as high-cost sensors, maintenance/update and provides a cost-effective solution. This article presents an efficient NILM-based energy management system (EMS) for residential microgrids. Firstly, smart meter data are analyzed with a multi-task deep neural network-based approach and the appliance-level information of the consumers is extracted. Both consumption and operating status of the appliances are obtained. Afterward, the energy consumption behaviors of the end-users are analyzed using these data. Accordingly, average power consumption, operation cycles, preferred usage periods, and daily usage frequency of the appliances were obtained with an average accuracy of more than 90%. The obtained results were integrated …