We should be able to TRAIN this!

technicalsupport

#41

Thanks. I’m betting the variable speed motors are the challenge. My old school Liftmaster garage doors are picked up by Sense like clockwork. They are one model year to early to be IoTable via SmartIQ.

I agree completely with you that Sense needs take a new tack, though on training, not detection. They need smarter feedback both from humans when detection goes askew, and from smart controllers when known devices are turned on/off. The trick with the former is to do it in a very structured way, so humans give Sense unambiguous training information. And the hard thing about the latter is building robust integrations with the dozens of automation APIs that users are likely to encounter.

I think we’ll see increased progress on the detection side for harder devices as the training feedback improves.


#42

http://community.sense.com/t/we-should-be-able-to-train-this/1483/35
@jackfurr, is the pool pump literally always-on? Can you turn it off at night and then it back on every morning? …maybe even a timer for 3, 4 , 6, or 8 hours off? I’ve owned a few pools and that is what I did only to save energy costs, yet I could keep my pool levels balanced… Power cycling could help a lot with pool pump detection, but while the pump is off Sense may find other devices.
… … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … V heart V


#43

Thanks for weighing in on this @mstraka606 and @kevin1 -

We did talk a bit in the webinar about the initiatives towards device detection, training possibilities, and integrations to support those efforts, which are absolutely the focus for us in 2018. Some of the work we’re doing to support integrations involves a pretty high development effort up front, but will mean that integrations with devices like smartplugs, connected thermostats, etc. will be much easier to implement down the road.

While the data science and engineering teams are knee deep in getting frameworks in place for these kinds of major detection improvements, there are smaller, parallel teams making sure the web and mobile apps are up-to-date and continuing to add new features, but know that the majority of our resources are focused on helping to make big strides in device detection and integrations this year.

Thanks to all of you for your patience and persistence as we keep working on things, and please keep the feedback coming!

Best-
Brad


#44

Brad awesome response. I’m very excited to hear about this focus. I can predict much improved performance! Thanks again for this new information.


#45

Wouldn’t it be easier to train the model for device detection if everyone entered their appliances by make, model, approx age, etc without directly tagging them and then the Sense ML would know to look for the same energy signature across a subset of households to identify that common appliance?


#46

Repost


#47

I was thinking the same thing. I’m not a machine learning expert, but It seems the whole idea is gather as much data as possible to paint a picture. If the machines can continue learning their patterns, and humans provide basic input, can’t we match the two together? Seems like more data is better than less.


#48

This question has come up many times over. Unfortunately, it’s just not that simple. Yes, we need data, but we need very particular types of data. It’s not a matter of Sense just knowing the signature of a device in isolation. Those signatures are unique, yes, but they’re unique in your particular home too. The common ML analogy for what we do is trying to pick out a particular voice in a room of people speaking. This is a pretty strong analogy, but it’s not perfect. That’s already a very hard problem, but what if that voice keeps changing in quality (in vocal range, in accent, in speed, and so on)? That’s essentially what we’re dealing with. The same device can look pretty different across homes based on other devices that are running concurrently as well as the general noisiness of your home power, which itself can vary over time. It’s tough to overstate how complicated and challenging this problem is.

Now, I will say that the points about make and model are solid because they do give us clues to work with. We allow you to do that in the Device Settings screen for detected devices we’d really love it you could do it for devices that you’re certain of. But still, they’re just clues and not 100% solutions.

We still do incorporate user input in other ways, just not in a manner where you can directly train Sense. Every time you utilize features like renaming, Community Names, marking devices as not on, and Network Identification, your input is directly helping us with device detection in your home and across our larger user base.

I hope that makes some sense.


#49

I agree completely that the uknown list should release the devices and allow the home owners to identify them (in a fraction of the time that it takes Sense to do so). It would still allow for the Sense DB to be built. I understand wanting the ‘unsupervised machine learning’ to do it’s job but right now it is way to slow. Let the end user help Sense and build up the DB quicker while Sense works on improving the code of it’s learning, context and electrical signature systems. Seriously, how long does it take to identify a heat pump or a refrigerator? Excellent product but just don’t understand the logic in preventing end users from marking devices themselves.


#50

I guess I don’t really understand this. If the sense was able to recognize something enough to be able to release it to you to label, then wouldn’t it know what it is so you don’t have to label it?


#51

This request makes no sense. There is no unknown list just a blob of noise. If something was identified it would be kicked back to the user as an unknown device.