  Non-intrusive load monitoring (NILM) identifies the status and power
consumption of various household appliances by disaggregating the total power
usage signal of an entire house. Efficient and accurate load monitoring
facilitates user profile establishment, intelligent household energy
management, and peak load shifting. This is beneficial for both the end-users
and utilities by improving the overall efficiency of a power distribution
network. Existing approaches mainly focus on developing an individual model for
each appliance. Those approaches typically rely on a large amount of
household-labeled data which is hard to collect. In this paper, we propose a
multi-appliance-task framework with a training-efficient sample augmentation
(SA) scheme that boosts the disaggregation performance with limited labeled
data. For each appliance, we develop a shared-hierarchical split structure for
its regression and classification tasks. In addition, we also propose a
two-dimensional attention mechanism in order to capture spatio-temporal
correlations among all appliances. With only one-day training data and limited
appliance operation profiles, the proposed SA algorithm can achieve comparable
test performance to the case of training with the full dataset. Finally,
simulation results show that our proposed approach features a significantly
improved performance over many baseline models. The relative errors can be
reduced by more than 50% on average. The codes of this work are available at
https://github.com/jxiong22/MATNilm
