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
this https URL

提出了一种用于家庭电器监测和功耗识别的非侵入式负载监测（NILM）方法，通过拆分整个房屋的总功耗信号来识别各种家用电器的状态和功耗。通过有限的标记数据和训练高效的样本增强方法，可以实现体现整体功耗监测的负载识别性能。

MATNilm: 多电器多任务非侵入式负载监测有限标记数据

MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited  Labeled Data

A convolutional sequence to sequence non-intrusive load monitoring model is
proposed in this paper. Gated linear unit convolutional layers are used to
extract information from the sequences of aggregate electricity consumption.
Residual blocks are also introduced to refine the output of the neural network.
The partially overlapped output sequences of the network are averaged to
produce the final output of the model. We apply the proposed model to the REDD
dataset and compare it with the convolutional sequence to point model in the
literature. Results show that the proposed model is able to give satisfactory
disaggregation performance for appliances with varied characteristics.

提出了一种卷积序列到序列的非侵入式负载监测模型，并成功地将其应用于 REDD 数据集来进行电器分解，并与文献中的卷积序列对点模型进行比较。结果表明，该模型能够为具有不同特征的电器提供令人满意的分解性能。