0:02

Latitude Media, podcast at the

0:04

frontier of climate technology. Hey

0:08

Chad, are you there? Hey, yeah, I'm here.

0:10

What's up? Back in May, OpenAI

0:12

released GPT-4-0, and it was a big

0:14

leap forward for voice and video interaction

0:16

with a chatbot. Hey, let's do, let's

0:18

have some fun. There are a

0:21

bunch of demos from OpenAI like this one,

0:23

where the chatbot is mimicking human emotion in

0:25

ways we really haven't seen outside science fiction.

0:28

Uh, I'd like you to be super sarcastic. Everything you

0:30

say from now on is just going to be dripping

0:32

in sarcasm. How does that sound? Oh,

0:36

that sounds just amazing. Being

0:39

sarcastic all the time is exhausting

0:41

or anything. I'm so

0:43

excited for this. Nope,

0:46

sarcasm. Let's get this party started.

0:48

Or whatever. Generative

0:52

artificial intelligence has come a long way

0:55

in just the last two years. Built,

0:57

of course, on many decades of research

0:59

before. A lot of the stuff

1:01

that is being leveraged today in the AI

1:03

world comes out of language models. And we

1:05

were doing very early days of speech recognition

1:07

and language models back 20, 25 years

1:09

ago. From the late 80s into

1:11

the mid 90s, Mike Phillips was a research scientist

1:13

at MIT. He worked on

1:16

speech recognition and natural language processing. Mike

1:18

founded multiple companies in the speech recognition space,

1:21

one of which built the voice assistant for

1:23

the Samsung Galaxy phone. In the early

1:25

days of these voice assistants, smartphones were

1:27

just coming out. We kind of

1:29

realized that the phones were getting connected to the

1:31

data networks and they were going to start

1:33

to be more like the internet and then

1:35

therefore become people's personal information,

1:37

communication, entertainment device. And we also

1:39

realized, well, you know, you

1:42

shouldn't have to like type on a little,

1:44

little keypad. You should be able to just

1:46

talk to these things. After selling both of

1:48

those companies, Mike turned to energy and he

1:51

asked what platform can unlock savings and decarbonization

1:53

that is analogous to the smartphone. He

1:55

co-founded Sense, which created an energy monitor

1:58

installed in an electrical panel. to

2:00

give real-time information on every device in a

2:02

home. Look, if smart devices were fully enabled

2:04

and every device out in your home told

2:06

us what it was doing, we'd be happy

2:08

to use that. But it's far

2:10

from being the case. And we realized that

2:12

if we could measure the power in a

2:15

detailed, left-right way, could we figure out what's

2:17

going on just from the power signatures? So

2:19

you can start to see where the similarities

2:21

come in, that we used to

2:23

do speech recognition on audio signals.

2:25

We are now doing electrical device

2:28

identification based on power signals. So

2:37

the company was started in 2013, and

2:39

then you had years of time deploying

2:41

devices in the field, and then you

2:43

discovered that suddenly you had

2:45

a lot of visibility beyond the home.

2:48

What kind of view of the grid

2:50

did desegregation uncover? The first thing we

2:52

did was collect some signals and realize,

2:54

oh, look, we can't do this unless

2:56

we have high-resolution data. Our

2:59

initial view is we had previously written

3:01

software for smartphones, we'd write software for

3:03

smart meters, and we'd be all set,

3:06

quickly found out that the existing smart meters just

3:08

did not have the data we needed. So that

3:10

let us down a long path that many of

3:12

you know that we started to build these little

3:15

orange boxes that go inside

3:17

electrical panels and collect data at super

3:19

high resolution, up to a million samples

3:21

a second in the little orange box.

3:23

So that high-resolution data was the key

3:25

to unlocking what happens in the home.

3:28

And now to your point, we realize

3:31

sometime afterwards that that same technical

3:33

capability, so high-resolution data, edge computing, real-time networking

3:35

that we use to interact with consumers on

3:38

what's happening in the home, we can look

3:40

the other direction and we can see what

3:42

the grid is doing in real time from

3:44

the edge. Are you saying that the first

3:46

class of smart meters weren't actually as smart

3:48

as we think they are? Hey, you said

3:50

it, not me. SENSE

3:52

is now focused on putting its machine-learning

3:54

technology into new generations of smart meters.

3:57

Mike is bullish on the role of advanced meters for

3:59

grid intelligence. but as utilities

4:01

start a new wave of rollouts,

4:03

he worries they aren't investing in

4:05

the right architecture. Most people think

4:07

of meters just as data collection

4:09

devices. You have to start

4:11

to change that mindset. And once you start

4:13

to think of this as a distributed platform,

4:16

not just a data collection device, this

4:18

entire world of making use of the data

4:21

at the edge and AI machine

4:23

learning at the edge starts to get opened up.

4:28

This is The Carbon Copy. I'm Stephen Lacy.

4:32

This week, a conversation with Sense CEO

4:35

Mike Phillips on what AMI 2.0 could

4:37

and should look like. Past

4:40

deployments of smart meters didn't bring the intelligence

4:42

promised. How do we avoid the same outcome?

4:51

America's green banks are preparing to unleash

4:54

a wave of capital for clean energy.

4:56

The greenhouse gas reduction fund invests a

4:58

historic $27 billion in projects nationwide. This

5:00

could mobilize up to $150 billion of

5:02

private capital for

5:04

solar, storage, efficiency, and electrification

5:06

in underserved communities. So

5:09

how do we deploy those billions quickly,

5:11

efficiently, and with the highest impact? On

5:13

July 18th, Latitude Media and Bandion Infrastructure

5:15

will host a virtual event exploring the

5:18

on-the-ground realities of making America's green banks

5:20

a success. Register for free by clicking

5:22

the link in the show notes or

5:25

go to latitudemedia.com/events. Clean

5:27

energy and climate tech are policy-driven industries

5:29

and anyone working in this field touches

5:31

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5:33

very real way. And that's why you

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political spectrum. Listen at Latitude Media. or

6:01

subscribe to the show anywhere you get your

6:03

podcasts. So

6:09

as you said, the first

6:11

generation of smart meters were just

6:13

seen as data collection devices. And

6:16

most people who look at the

6:18

rollout of AMI 1.0

6:20

would say it did not live

6:23

up to expectations. Ultimately,

6:26

what do you think the limitations of the

6:28

first rollout of smart meters was? Was it just that

6:31

the devices themselves weren't powerful enough? Was

6:33

it that it was the wrong infrastructure?

6:36

Was it just as a data collection

6:38

device, the utilities didn't know what to

6:40

do with that data? And they had

6:43

an inability to use it properly. Give

6:45

us your broad view on what went

6:48

wrong with AMI 1.0, which

6:50

I think most people think wasn't that

6:52

successful. Yeah, look,

6:55

I think it was basically the wrong

6:57

architecture. Look, it made sense back in

6:59

2008 or whenever that happened, but it

7:01

hasn't transitioned since then. So

7:04

by wrong architecture, look back to my

7:06

telecom example, imagine if the way Google

7:09

Maps on your phone worked is

7:11

your phone would collect 15 minute interval

7:13

data of your location, send it up

7:16

in batches to your telecom

7:19

provider, who would then make it

7:21

available through what gray button instead of green button or

7:23

something like that. And then applications like

7:25

Google Maps could get that data a

7:27

day later and then

7:29

do something with it. Well, what would Google Maps on your

7:31

phone be? It would be like a static map and

7:34

maybe a monthly historical report of your

7:36

traffic on your route to work and

7:38

maybe compare your neighbors, how

7:40

you drive compared to your neighbors. But would

7:43

you use that app? You

7:45

might use it every now and then, but you would not engage

7:47

in it in the way you do with Google

7:49

Maps. And what I just

7:51

described exactly matches AMI 1.0

7:54

architecture, low resolution data sent up to

7:56

the service provider, made available later on

7:58

in the day. And look, there's some

8:01

things you can do with that, but you just can't

8:03

have a real-time consumer-facing

8:05

app, and then you can't

8:07

see the grid in real time from the edge either. So

8:10

we're at a point now where we're

8:13

rolling out a lot of new meters.

8:15

We've had a decade to develop the

8:17

architecture. What is your sense now for

8:19

the current architecture that utilities are reinstalling?

8:22

We're crossing the threshold now. Look,

8:25

there are meters available today on the market

8:27

that do all the stuff we want to

8:30

be able to do and unlocks all this

8:32

potential. And by unlocking potential,

8:34

let me circle back to something

8:36

that you all are quite into,

8:38

and everyone's talking about AI for

8:40

the grid. Well, look, we

8:42

know AI is driven mainly by

8:44

machine learning-based approaches these days, and

8:47

that's mainly driven by data. And

8:49

if you don't have the right data, you're

8:51

kind of stuck. And there's a lot of

8:54

talk in the industry about grid edge intelligence,

8:56

but people are mainly talking about taking data

8:58

from the edge, this 15-minute interval data, processing

9:01

it in the cloud. And there's some things you

9:03

can do with that. I'm not denying that. But

9:05

to fully unlock the potential for AI for the

9:07

grid, we need the right data.

9:09

And this is what we learned long ago

9:11

at Sense, to have a real-time consumer experience.

9:14

By real-time, I mean you turn on your

9:16

microwave and it shows up in the app

9:18

a second later. To have

9:20

that kind of experience, you can only do

9:22

it with high-resolution data. What we've learned since

9:24

then is that same high-resolution data lets us

9:26

see the grid from the

9:28

edge, lets us see transformers arcing, lets

9:30

us see vegetation hanging power lines in

9:33

real-time. That only happens through high-resolution data.

9:35

So sorry to keep going on

9:37

about this, but the number one step is to

9:39

get the right data in

9:41

the meters themselves and be able to

9:44

process it there. Well, I know you've

9:46

been somewhat disappointed with how some utilities

9:48

are reinvesting in their metering networks. Is

9:50

that just because they're essentially investing

9:53

in a technology that is not much

9:55

better than the first generation? Yeah,

9:57

we are certainly worrying about

9:59

this. that there's a big opportunity

10:01

now that, like I say, there are meters

10:03

available today that can provide a lot of

10:06

headroom for what happens in the future. And

10:09

there's still decisions being made for a

10:11

previous generation of meters. So we're trying

10:13

to help utilities and help others. And

10:16

in fact, we're just publishing up on

10:18

our website an AMI buyer's

10:20

guide. And look, we don't have

10:22

the full picture of all the things that a

10:24

utility needs to consider for AMI deployments, but we

10:26

do know a lot about how

10:29

to deploy data, intelligence,

10:31

AI at the edge of the grid. And

10:34

so we're putting together the point of

10:36

view of all the things you need.

10:38

High resolution data, enough computation, and

10:40

the ability to have real-time networking are the

10:42

three things you need, but you've got to

10:44

pay attention to the details and get it

10:46

right. So give us a sense for what

10:48

the generation of meters that you're working on

10:50

can give us in terms of data versus

10:54

the traditional meters. The

10:57

most basic thing is to get high

11:00

resolution data of voltage

11:02

and current in the grid. Let's

11:05

just see what's happening at home. Let's just

11:07

see the grid and buy that. Sorry to

11:09

go into some of the techie details, but

11:11

this means continuous sampling of these voltage and

11:13

current waveforms of at least, there's

11:15

meters on the market that are doing that at

11:17

15,000 times per second. So that's 50

11:20

million times more data than AMI 1.0. And

11:23

again, for those of you who get scared, we don't even know how to

11:25

deal with AMI 1.0 data, we don't

11:27

mean you send all that up to the network,

11:29

you're processing at the edge. So with 50 million

11:32

times more data, we see the first 100 harmonics

11:35

of signals at the edge, and that is what

11:38

we need to see what's happening inside the home, see what's

11:40

happening in the grid. So

11:42

those meters are available today. Utilities can

11:44

go get rate cases approved

11:46

and be able to deploy them, and

11:48

it's happening in pockets right now. And

11:52

look, I know there's been a lot of talk,

11:54

and we're involved in this too, about what comes

11:56

after that also. So we're starting to work with

11:58

meter makers to go... all the

12:00

way up to a megahertz sampling. So

12:03

there's progression and things are happening in this

12:05

world, but there's meters on the market today

12:07

that are sufficient for doing what we do.

12:10

And we wanna make sure that utilities are

12:13

fully equipped with the information about that so

12:15

they don't go by the previous generation. On

12:23

July 18th, join Latitude Media and Banion Infrastructure as

12:26

we take a deep dive into the next phase

12:28

of deploying the $27 billion greenhouse

12:31

gas reduction fund. We'll provide practical insights

12:33

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12:35

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12:38

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12:40

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12:42

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12:44

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12:47

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12:49

Amanda Lee, and Clean Energy Fund of

12:51

Texas EVP Billy Brisco will answer these

12:54

questions and more on July 18th at

12:56

1 p.m. Eastern. This is a must-attent

12:58

for project developers and financiers. Register for

13:00

free at latitudemedia.com/events or click the link

13:03

in the show notes. I'm

13:06

Julia Piper. I'm Brandon Herbert. And I'm

13:08

Emily Dominich. A little over a year

13:10

ago, political climate took a break so

13:12

we could focus on the groundwork of

13:14

implementing America's biggest ever climate bill, the

13:16

Inflation Reduction Act. I'm excited to say

13:18

political climate is back. And I'll be

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joined by my two co-hosts to riff

13:22

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13:24

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13:26

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13:28

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13:32

climate is a show for people who

13:34

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13:36

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13:38

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13:40

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13:42

join me, Brandon and Emily every other

13:44

week starting in April for fresh episodes

13:46

of Political Climate. Subscribe to the show

13:48

wherever you listen to podcasts. So

13:51

GPU chip maker Nvidia is working with

16:00

people can see what's going on in their home and track

16:02

down energy hogs, we call them in the home. It

16:05

also is having a big

16:07

impact on people's participation in

16:09

demand flexibility, load flexibility. How

16:11

do you deploy time-infused rates or demand

16:13

charges if you can't let the users

16:16

see what's happening in real time in

16:18

their homes? And then we're

16:20

also using this high-resolution, detailed view of what's

16:22

happening in homes for helping the electrification, find

16:24

the homes that are best candidates for heat

16:26

pumps and so on. Okay, so

16:29

that's the consumer side. What about the grid side? Well,

16:31

this is actually a surprise for us.

16:34

We did not actually realize that the

16:36

edge of the grid is largely unmonitored.

16:40

And again, the technical capabilities in

16:42

these new meters, high-resolution data, edge

16:45

computing, real-time networking that

16:47

we used to see inside the home, we can see the

16:49

other direction to the extent that

16:51

when things happen on the grid, if it

16:53

shows up as power fluctuations in the home,

16:56

we see at the edge. And

16:58

when you just look at power once a second, which

17:00

is what was done even in the internals of

17:03

AMI 1.0, you don't

17:05

see that much. You see big

17:07

level shifts whether conservation

17:10

voltage reduction is going too far and so off.

17:13

You could do that with AMI 1.0. As you get

17:16

to high-resolution data, even tiny

17:18

little arc flashes that are happening on

17:20

the secondary side, so transformers that are

17:22

arcing problems in

17:25

the meter socket and stuff like this, we

17:27

see all that in detail. And

17:29

things like vegetation hitting primary

17:31

lines. We see

17:33

the entire, all the way up through the distribution

17:36

system and all the way into the transmission system.

17:38

So when things happen, we see it

17:40

at the edge. So what are

17:42

the consequences of getting these meter

17:44

rollouts wrong? These rollouts tend to

17:46

happen on around 15-year cycles. So

17:48

the first cycle was between 2008,

17:50

2011. And

17:54

so now we have a bunch of utilities that need to

17:56

reinvest, as we've said. What are

17:58

the consequences for... It's

22:00

got some very particular details that we've been

22:02

deep into. We

22:05

also are seeing the parallels of what's happened

22:07

in audio processing and so on. We

22:09

actually have someone coming from leading

22:12

these efforts at Google is going to be

22:14

joining us shortly as an advisor for the

22:16

company. He's really helping

22:19

us take all the latest

22:21

techniques of what's happening in the big

22:23

tech companies and apply it to this

22:25

particular problem. You also

22:27

have a new generation of meter that's coming

22:29

out as well, right? As

22:32

I said earlier, the meters on the market

22:35

today are great. They support,

22:38

like I say, we can process up to

22:40

the 100 harmonics of the power signals. It

22:42

supports the use cases that

22:44

we see out there today. Technology

22:48

is always advancing. We

22:50

are working with some of the meter makers to have

22:53

the next generations of meters that have even higher sampling

22:55

rates than that going all the way up to a

22:57

megahertz. That's unlocking some

22:59

more use cases around arc fault

23:01

detection. When there's an arching on

23:03

a transformer or something like that, it shows up in

23:05

these high frequencies. If you start to

23:08

worry about even a broader set of use cases, continue

23:11

to push the envelope on data

23:13

and, as you mentioned, more and

23:15

more computation. It's a tricky topic.

23:18

This happens in every technology field. We

23:21

don't think you should wait because the latest generation

23:23

is great. If you're making a meter decision now,

23:25

you need to replace your meters. There are meters

23:27

on the market that do what we need to

23:29

do. The next step

23:31

is coming here shortly. Mike Phillips,

23:33

co-founder and CEO of Sense, thank you so

23:36

much. Thank you, Steven. That's

23:45

going to do it for the show. If

23:47

you want to learn more about AI and

23:49

smart meters, we're covering that subject in particular

23:51

and AI generally at latitudemedia.com. You can subscribe

23:54

to our newsletter there. You'll get all our

23:56

editorial coverage in your inbox in the middle

23:58

of the week.