Like I said I share your frustrations. I didn’t join this community as a pleasant person. I joined because of necessity because I had a defective CT and I was quite the unhappy camper.
Over time I’ve understood a bit more about the team and that they actually really care about making it “right”. So I’ve stuck around and offer some advice when I can here and there based on what I’ve learned. I’ve also screwed over some people probably with my incorrect assumptions, but let’s imagine that never happened.
I worked with the IBM Watson team many years ago and would frequently sit in a sky scraper in SF going over how AI works. I’m not assuming you don’t know yourself, but the basics of it is this. You have to have really large sample sets in order for it to work. You also have to have ground truths, in other words you have to correlate something to a fact. This is basically what you are asking to do, correlate an electrical signature to a fact. The bad part about doing that is that you can screw up the whole thing for everyone else if you’re not 100% sure. I’m not assuming you would mess it up, but I can guarantee one of their customers would. It would probably be me because I like jumping the gun.
So let’s say 100 customers have sump pumps and 75 have a Grundfos brand, Sense can probably figure out after looking at a lot of energy signatures that match that it’s something that’s recognizable so it asks in the app, hey what is this. Customers then say “oh that’s my pump” and then they have some ground truth. But if you have that one single odd ball pump, there’s nothing to compare to, so it just holds that signature in it’s database waiting for more and more and more.
As Sense grows as a company the data will grow and the recognition of devices will be better. I hope that in a few years it will be much better and I’m confident that it will be.