A Self-training Multi-task Attention Method for NILM
Keqin Li, Jian Feng, Yitong Xing, Bowen Wang
Abstract
The key task of non-invasive load monitoring (NILM) is to know the power consumption of all household appliances, from which the power consumption of individual household appliance can be disaggregated. The power consumption and on/off state of household appliances are expected to be obtained, the multi-task learning model is used. One task is used to train the power comsumpution of household appliances, the other task is used to train the on/off state of household appliances, and then these two results are combined as the final result. In this paper, a self-training multi-task learning model is proposed. In the model, a parallel structure is used to deal with two different tasks, and the outputs of two branches are directly combined as the final output. The model only needs one loss function and is only trained once. In addition, we also introduce attention mechanism into the proposed model. Finally, two public data sets are simulated to verify the effectiveness and superiority of the proposed method.