The Exchanges

Q the devices themselves weren't powerful enough? Was it That it was, so it was the wrong infrastructure? Was it just as a data collection device, the utilities didn't know what to do with that data? And they, you know, they, they had an inability to use it properly. Give us your broad view on what went wrong with AMI one point, which I think most people think wasn't that successful.

A Yeah, look, I, I think it was basically the wrong architecture. Look, it made sense back in 2008 or whenever that happened, but it hasn't transitioned since then. So, and by wrong architecture, look, back to my telecom example, imagine if the way Google Maps on your phone worked is your phone would collect 15 minute interval data of your location, send it up, ah, in batches to, to your telecom provider, Who would then make it available through, what, gray button instead of green button or something like that, and then applications like Google Maps could get that data a day later and then do something with it. Well, what would Google Maps on your phone be? It would be like a static map and, and maybe a monthly historical report of your traffic on your, your route to work and maybe compare your neighbors, uh, how you, your drive compared to your neighbors, but would you use that app? Like, you might use it every now and then, but you would not engage in it in the way you do with Google Maps. And what I just described exactly matches AMI-one.o architecture. Low-resolution data sent up to the service provider, made available, uh, later on, and look, there's some things you can do with that, but you just can't have a real-time, consumer-facing app, and then you can't see the grid in real-time from the edge either.

AI assessment note: “Yeah, look, I, I think it was basically the wrong architecture.”

Q So let's apply this to actual use cases out in the power sector. What were some of the successful use cases in AMI one point O and what do you think the new use cases for this next generation of smart meters are?

A So let's, let's start with the consumer side on the consumer side with AMI one dot O you could provide a kind of next day view, uh, you know, of how power was used in your home yesterday. So, so like this was used in these various portals that utilities provide that give People have some insights into how their bills tracking, which is a big leap compared to waiting to the end of the month before you have any idea what's going on in your home. Look, frankly, not a lot of people use those portals. I think there's pretty well defined metrics that these haven't got a lot of use. Once you get to this new architecture, applications like Sense become possible where we can provide consumers with their real-time, ah, detailed view of what's happening in the home, and this becomes super relevant for, ah, energy efficiency. People can see what's going on in their home and track down energy hogs, we call them in the home. It also is having a big impact on people's participation in Demand flexibility, load flexibility, you know, how do you deploy time of use rates or demand charges if you can't let the user see what's happening in real time in their homes. And then we're also using this high resolution detailed view of what's happening in homes for helping the electrification. Find the homes that are best candidates for heat pumps and so on.

AI assessment note: “with AMI one dot O you could provide a kind of next day view”

Q So GPU chip maker NVIDIA is working with a company like Utilidata to add a lot more processing power into meters. Do we need a lot more processing power to, to process this amount of data that you're talking about?

A Yeah, so, uh, we're paying a lot of attention to that too, and, and look, I, I will never argue against more processing power. You know, this is the, the lessons we've learned. More data, more processing is what's been driving this, these revolutions in, in AI. We're actually been more concerned about the access to the data. We, we know what we can do with more data. You know, we, we've, since we've had this kind of high resolution data all the way up to megahertz for years now, we, we know what we see in that data, and we know how to use it. On the computation side, sure, we want more computations. GPUs and meters, when they become practical, we will absolutely make use of. I've been a little less concerned about that, because for computation, you can always be very efficient about your algorithms, and you can make trade-offs from the algorithms. The reason we're more concerned about the data is if you don't have the data, you don't have the data. There's just nothing you can do if you don't have the data.

AI assessment note: “On the computation side, sure, we want more computations.”

Q Well, I know you've been somewhat disappointed with how some utilities are reinvesting in their metering networks. Is that just because they're essentially investing in a technology that is not much better than the first generation?

A Yeah, we are certainly worrying about this, that there's a big opportunity now that, like I say, there are meters available today that can provide a lot of headroom for what happens in the future. And there's still decisions being made for a previous generations of meters. So, so we're, we're trying to help utilities and help others. And in fact, we're just publishing up on our website, kind of an AMI buyer's guide. And look, we don't have the full picture of all the things that a utility needs to consider for AMI deployments, but we do know a lot about how to deploy data, intelligence, AI at the edge of the grid, And, and so we're putting together kind of the point of view of all the things you need. High resolution data, enough computation, and the ability to have real-time networking are the three things you need, but you got to pay attention to the details and get it right.

AI assessment note: “Yeah, we are certainly worrying about this, that there's a big opportunity now”

Q So the company was started in 2013, and then you had years of time deploying devices in the field, and then you discovered that suddenly you had a lot of visibility beyond the home. What kind of view of the grid did disaggregation uncover?

A The first thing we did was collect some signals and realize, oh, look, we can't do this unless we have high resolution data. And, you know, our initial view is we had previously written software for smartphones, we'd write software for smart meters, and we'd be all set. Quickly found out though that the existing smart meters just did not have the data we needed. So that led us down a long path that many of, many of you know that we started to build these little orange boxes that go inside electrical panels and collect data at super high resolution, up to a million samples a second in the little orange box. So that high resolution data was the key to unlocking what happens in the home. And now to your point, we realize, uh, sometime afterwards that that same Technical capabilities, so high resolution data, edge computing, real-time networking that we use to interact with consumers on what's happening in their home, we can look the other direction and we can see what the grid is doing in real time from the edge.

AI assessment note: “we can see what the grid is doing in real time from the edge.”

Q So we're at a point now where we're rolling out a lot of new meters. We've had a decade to develop the architecture. What is your sense now for the current architecture that utilities are reinstalling?

A We're crossing the threshold now. Look, there are meters available today on the market that do all the stuff we want to be able to do and unlocks all the potential. And by unlocking potential, let me circle back to something that you, you all are quite into, which, and everyone's talking about AI for, for the grid. Well, look, we, we know AI is driven mainly by machine learning based approaches these days, and that's mainly driven by data, and if you don't have the, the right data, you're, you're kind of stuck, and there's a lot of talk in the industry about grid edge intelligence, but people are mainly talking about taking data from the edge, this 15 minute interval data, processing it in the cloud, and there's some things you can do with that, I'm not denying that, but to fully unlock the potential for AI for the grid, We need the right data. And this is what we learned long ago at Sense, to have a real-time consumer experience. By real-time, I mean you turn on your microwave and it shows up in the app a second later. To have that kind of experience, you can only do it with high-resolution data. What we've learned since then is that same high-resolution data lets us see, ah, the grid from the edge, lets us see transformers arcing, lets us see vegetation hanging power lines in real-time. That only happens through high-resolution data. So, Sorry to keep going on about this, but…

AI assessment note: “people are mainly talking about taking data from the edge, this 15 minute interval data”

Q So you've been involved in machine learning, natural language processing for decades now. What are the biggest advancements that you think are going to push the energy transition forward?

A So, let me tell you a little story. Like, Again, we came out of this natural language world. We used to train language models on, like, a million words of text. They could predict the next word based on the last two or three words. That was fine for what's the weather in Boston tomorrow. But what's happened since then is, like, GPT has trained on, like, a million times more data. Like, when you add a million times more data, like, amazing things happen. Now these things can predict the next word based on the last 30,000 words and now you have Context, meaning, emotion, domain knowledge, all built into that thing. So the lesson from that world is the, the power of data and the, the, the techniques that are layered on top of these to find amazing patterns within this data. So that's what we are, we are leveraging here and, and be able to make use of the, the huge amount of data we can now process at the edge. And we can't just collect it. We have to process the edge. Combined with the latest techniques, and we're, we're getting increasingly plugged into all the latest techniques that have come from this space.

AI assessment note: “the lesson from that world is the, the power of data and the, the, the techniques”