I’m Ali from the Neurio team. I thought I should chime in.
@gray thank you so much for starting the conversation. And for taking the time to respond to questions.
First a bit about myself. I studies pattern analysis at university. Half of our team at Energy Aware are masters and PhD’s in that subject. And the other half takes care of our hardware and firmware.
I should point out that we are not competing with SmartThings at all. We’ve already had great conversations with the ST team and are very excited to work together. The way we see it, ST guys have done a great job making smart devices a reality. We don’t want to do something that’s already done well. We like to add value to ST users by bringing in the idea of “home intelligence.” Nest showed the value of leaning things, but why stop at a thermostat?
If you think about, wires in our homes are analogous to a nervous system that connect everything to a central place. Except, in that central place (the breaker panel) there’s no intelligence – no monitoring of what’s actually going on. That’s what Neurio likes to be. If Neurio can watch the home as a whole, it can put the pieces together and understand user behaviour and intent. It can then share that with the user through notifications (your washer is done but dryer hasn’t started, or your oven is still on), or share it with connected devices like ST (no one’s home, so let’s adjust the thermostat and turn off vampire loads).
Admittedly, this is a new concept in many ways, so I completely understand any skeptical views. Hence why I am here!
@chrisb, regarding the algorithms, you’re absolutely right that we cannot see every single device, or tell the lights apart. But for much of what we’ve discussed, you actually don’t need that level of granularity. Detecting if a home is at its baseload is not difficult, so the moment something is turned on (an unusual spike in the baseload power), we know that there’s presence (someone just got home). It didn’t matter which light or switch it was – we can still deduce that someone’s gotten home to turn that switch.
Now let’s add some historical modelling into the mix: looking at the last month, Neurio could detect the hours user leaves home and comes back, goes to bed, etc. (based on watching when the home is at its baseload pattern). So once Neurio notices an event, it can also validate it through its historical models to see if what it has detected “makes sense”.
When it comes to pattern analysis, it’s not just about permutations of appliances adding up to a certain power level. We monitor each phase, which cuts down overlaps by half. We then look at some obvious parameters, such as the jumps on the power data. As it happens, within the same home, there’s usually not that many major appliances that have a similar power spike. Your dryer is likely 4kW, your dishwasher more like 2kW and your washer more less than 1kW. So that already gives us a quick idea of what we should be looking for. We also consider external parameters such as time of day or the weather (a sudden 5am spike is less likely to be AC). We can then introduce things like power factor, or frequency of change in the signal, the duration of usage, the shape of the pattern, the energy use (not just power), etc.
We have a couple of videos that visualize the algorithms to the best we could, so feel free to check them out. We’ve been working on these algorithms for two years. And we’re going to keep working to make them better.
@twack, sorry if the video is vague. We tried to get into more details in the content page. I’d be more than happy to get even more technical here, if you there are any questions I can help with.
Thanks again for the conversation guys. Check out our campaign; I’ll keep an eye for more questions: Neurio: Home Intelligence