  The modern artificial intelligence techniques show the outstanding
performances in the field of Non-Intrusive Load Monitoring (NILM). However, the
problem related to the identification of a large number of appliances working
simultaneously is underestimated. One of the reasons is the absence of a
specific data. In this research we propose the Synthesizer of Normalized
Signatures (SNS) algorithm to simulate the aggregated consumption with up to 10
concurrent loads. The results show that the synthetic data provides the models
with at least as a powerful identification accuracy as the real-world
measurements. We have developed the neural architecture named Concurrent Loads
Disaggregator (COLD) which is relatively simple and easy to understand in
comparison to the previous approaches. Our model allows identifying from 1 to
10 appliances working simultaneously with mean F1-score 78.95%. The source code
of the experiments performed is available at
https://github.com/arx7ti/cold-nilm.
