Non-Intrusive Load Monitoring (NILM) seeks to save energy by estimating
individual appliance power usage from a single aggregate measurement. Deep
neural networks have become increasingly popular in attempting to solve NILM
problems. However most used models are used for Load Identification rather than
online Source Separation. Among source separation models, most use a
single-task learning approach in which a neural network is trained exclusively
for each appliance. This strategy is computationally expensive and ignores the
fact that multiple appliances can be active simultaneously and dependencies
between them. The rest of models are not causal, which is important for
real-time application. Inspired by Convtas-Net, a model for speech separation,
we propose Conv-NILM-net, a fully convolutional framework for end-to-end NILM.
Conv-NILM-net is a causal model for multi appliance source separation. Our
model is tested on two real datasets REDD and UK-DALE and clearly outperforms
the state of the art while keeping a significantly smaller size than the
competing models.

使用深度神经网络进行实时，多设备源分离的，全卷积的非侵入式负荷监测，优于现有技术