Estimation of Target Appliance Electricity Consumption Using Background Filtering

Measurement of the electricity consumption of major appliances in different time segments is of crucial significance to demand-side management and energy conservation. Non-intrusive load monitoring (NILM) can infer the target appliances' power use information by only collecting and analyzing the aggregate power data at the single power entrance point. Inspired by the success of deep neural network in other fields, some researchers have applied it to NILM with promising results. However, existing studies require labeled real aggregate data to train the networks, while time-synchronized measurement of the target appliance for labeling is hard to achieve in practice. This paper proposes to train networks with only synthetic aggregate data. Furthermore, a training data generation method via background filtering is proposed, and the obtained training data is used to train the network for estimating electricity consumption. This generation method only needs unlabeled real aggregate data and the target appliance's operation curves, which reduces the difficulty of training data acquisition. The proposed estimation method achieves higher accuracy than current methods in tests on a public dataset which also demonstrates the effectiveness of background filtering.