Electronic – How does electrical signature devices work when there is more than one load connected

energypowersensor

Now more than ever I see energy monitoring devices like this one that uses "AI" to detect the loads connected to your home electrical network, like the washer or a hairdryer.

These devices use electrical signature measurements and compare them with a large database in the cloud to predict the type of load. Electrical signature measurements is not a new tech, we engineers had been using it to detect anomalies for example in motors and other loads as a nonintrusive means to explore the load.

You create a signature by collecting information about the frequencies with a fast Fourier transform, the phase, and power. And that's not rocket science. What really puzzles me is how can you identify a signature when there are several loads connected? I mean, I can create an individual electrical signature for my dishwasher, my 2 TVs, my Xbox and my fridge, but if several of those are running together the signature properties add so I will need to try every possible combination of signatures to predict what devices are powered on? Is it that brute forced the way these devices work? or there is an elegant solution to the problem?

Best Answer

It looks like they have 3 ADC's so they are doing more than current monitoring, and a CPLD, which probably means they have real-time constraints and need even sampling.

enter image description here Source: http://whatnicklife.blogspot.com/2017/12/sense-energy-monitor-teardown-sampling.html

Could there be a difference in this circuit? Souldn't be do hard from only looking at the graph of the voltage and the current.

schematic

simulate this circuit – Schematic created using CircuitLab

Even refrigerator doors can be detected opening: enter image description here
Source: The Determination of Load Profiles and POwer Consumptions of Home Appliances

On top of that each SMPS has a frequency signature from noise and harmonics.

How they are actually formulating the AI and neural network? Ask them.