Abstract
In daily life, it is easy to obtain the power consumption information of a house by installing an energy meter at the entrance. The non-intrusive load monitoring method can decompose the power consumption and the working state of a specific load according to the aggregated information of the power inlet, and then help people to save electricity. Here, we present a novel and complete non-intrusive load identification algorithm based on deep neural network and differential current. The algorithm includes several modules for data preparation, event detection, feature extraction and load identification, and uses the BLUED dataset to test the feasibility of the proposed algorithm.
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