Hi everyone, thanks for coming. This is my second year at Foss Dam, this is my first time
talking, so thank you very much for listening to me. How's everyone feeling Friday
afternoon, can I have a Sunday afternoon, sorry, no no, yeah, okay, I'll try and
I'll try and keep the energy up. I'm Peter Dodford, I'm head of technology at Open Climate
Fix and it's quite a long title, but I'm going to talk to you a little bit about what
we're up to and some of the challenges we have and I'm going to try and do a little
live demo for like today's a good excuse to do a live demo. Yeah, so as I said, I'll give
a little introduction into what Open Climate Fix are and what we do, why we've focused on
solar forecasting, we do some other things as well, but mostly solar forecasting and are kind
of flagship thing, cook quarts solar a little bit more and kind of we'll get to the end
on some new things that we want to do. I'm going to try and talk for 10 minutes and then
there'll be some time for questions as well. So Open Climate Fix, we have founded five
years ago, I haven't seen this gift thing before, so I might get distracted and there's
a few people you'll see in the pictures around the room here today. We're not for
profit, we're focusing, we're doing open-source solutions, we're focusing on AI solutions
to help decarbonise the grid. Various experience across the board are two founders pop
up there and I think some of you might know them. It's 15 of us in London, we've got one
person in India as well and that's some projects we've got out there. I'm actually a
Manchesterer and I end up taking the train quite a lot down to London. So why solar,
someone today said the world is on fire and this sign makes me think, can I borrow that
sign? I think it's 10 minutes left. I think if that's the doomsday clock, we've got to
do stuff, we've got to make stuff happen. We focus on solar. I think this maps quite powerful,
it kind of shows what's the cheapest source of energy in the world and once you get to
2030, it's basically solar everywhere, apart from maybe like Norway or Sweden where I'm
pretty sure that must, oh, I thought it might be hydro, but yes, so there's getting cheaper
and there's going to be more and more of it around. You can see this already happening. There's
loads of solar being built. There's loads of wind being built. My career started in the wind industry
so I'm still a little part of me that I want to see the wind carry on going. Yeah, this is from
member who we sometimes share an office with, so I trust them. And that's great. We're building
loads of solar loads of wind, but whether suddenly it starts to become a problem is going to be
lots of grids around the world that are now dependent on the weather systems that are coming
around. We're getting big fluctuations in energy. It's so different from having a coal power station,
having a gas power station. So we focused on, that's the reason why we focused on solar.
So our main thing is, is quartz solar. This is a UK solar forecast. You can scan that and it
should log you onto the app and you'll take it in if you want to. We've been taking lots of
satellite images, lots of weather data, training our ML model called PVNet. And then we show it
as an API. There's a nice front end that you can log into and see. We're delivering that to NISO.
So that's the UK TSO. And there's a few other customers as well that we use there.
We believe in this probabilistic values as well. It's never certain
and how the users use that probabilistic values as important. NISO of USTAR,
solar forecast in their demand forecast and showing the kind of an 8% improvement in accuracy.
And they've also used it in a reserve setting project where it can be shown to save
100 megawatts of reserves. So you don't have to have that 100 megawatts on. And that can
you can start to like calculate rough CO2 figures with those of those amount and we think it's
like 200,000 tons of CO2 year. For me, that's the so important. When I joined maybe three or four
years ago, it was always like, oh, we're going to do a solar forecast. But what's what's the impact?
What's going to happen? And starting to get those numbers out is important. It's impactful.
We can then work on the right things.
Something we've been working on recently is a Manchester prize. And that is to specifically
forecast satellite images. And then we put the predicted satellite images into PV net.
There's just a lot of data with all these satellite images. There are every five minutes
and getting them all together and getting them in the right way and using them.
It's shown we've given a five percent improvement by using that method. So if I
done ML engineer called James, he's been working on that for a year and it's, you know,
now it's starting to pay off, which is great. They've got a video of that.
So this is the true satellite images and then the cloud forecasting ones on the right
hand side. And I love looking at them. They're fascinating. And you see the different weather,
patterns and different things. And probably notice it's a bit more blurry on the right hand side.
And that's definitely one of our challenges is, you know, do we not want it blurry? Does that
actually capture some sort of uncertainty? Yes, it's an interesting problem to have.
Oh no. There we go. So here's some of the challenges. It's been data, the best definition
I heard for big data was like, it's not on your laptop. So we've just got loads of it and we always,
you know, every, we fill it up and we have to remove stuff and make sure we've got enough and
it's stuff and soul is somewhere here is managing all of that and it's, yeah, it's a lot of work to
do. A lot of that data isn't open source, the weather, the satellite, but the life satellite.
And that makes it hard for the community to kind of take part and we want the community to take part.
So we'll come back to that at the end. Training these models is hard to take a lot of power.
There's a lot of data that we have to get in the right format and it takes time.
And I mentioned about blurriness and, yeah, that's kind of one of the other challenges we've had.
I'll do a quick demo. It was the baiting of I have time.
So we do a site level forecast as well. So we've been doing a national one but we're also
do a site level. This is completely free and open source and anyone can use it and who knows
is Python here. But I can just paste that and this is where we are right now in Brussels and
it should be able to just do that and it will produce a solar forecast of here right now for the
next 48 hours. That's taking in weather, weather, open source, weather, kind of input.
If you slightly change some inputs then you end up with a few lines and maybe that gives some
uncertainty and things. I think I run that about an hour ago while sitting in the audience.
We launched this last year at Boston. Right here you have part of that talk and
it's great. Get him, try it out. Let us know. Let us know if it works. Should work all over the world and
yeah, it's cool. So I mentioned that problem of the weather data as private. We can't share that
with open source contributors. That makes it hard to develop models together. We've opened a
new project called Open Data PV where we're trying to train that PV network, PV net on completely
open source, weather data. So GFS and the freeze, MWF. We want to expand. We've done the UK,
we do some work in India but we want to expand everywhere. We want to produce a solar forecast
for lots of countries around the world. We want people to be involved and we need data engineers
and ML engineers and all the way along the pipeline. So this will almost definitely be a G-SOP
project so hopefully lots of other people are being involved as well. We re-wrote our ML pipeline
because the old one we had was really hard and really bad. It's been great to just do it
and get feedback instantly from the community. We've got more contributors to the new one than the
old one already and it's only been alive for two months or so. So it's worth redoing it and we're
excited by that. We've been trying to do some office hours so people can join like a live stream.
We just talk and ask questions and MLE's been organizing that so thank you MLE. Just investing
in the community trying to get people talking and working together. I said I don't need to talk
for 10 minutes. We can still 11 minutes. So we've got some time for questions if anyone has
a model architecture. Oh yeah so the question is what kind of model architecture for this one?
Paving it. It's a late fusion multimodal model so there's some CNN layers and it kind of all
comes together basically. We've tried to try a few other different ones and there's...
Yes we've tried on some very simple. Oh let me repeat the question. Can I expand on the other ones
that we've used? We've we've you know we start with XT Boost and something like that that just
can't give the performance basically. We've tried some I think we've tried some transformers
quite similar to what we have at the moment. There's a few of it. It's almost like tweaking
different things and in some ways doing these things actually give a bigger impact than tweaking
that model by you know forecasting or you know using more weather data. So it's a little bit
of like where do we invest time in? Any other questions?
Okay so once you know solar and power forecast how do you use that information?
So you get down to the type of money. So I mean what is advantage how I guess the advantage of
yeah so I guess there's from a house or a repeat question. How do people actually use the solar
forecast like why why why why are we bothering doing it? There's from a good stability point of
you you need a good you need to know you know roughly where the solar is going to be in the next few
hours so you can plan to put on other other plants and things like that otherwise you have to have
a lot of reserve, a lot of spinning reserve, a lot of plants just being at 50% just in case it changes.
So if you can provide a better forecast you can have less of those reserve power plants.
On a domestic level if you know how much solar there is going to be you know you plan how much
I'm going to charge my battery overnight how am I going to when are we going to charge my EV thinking
about dynamic prices and things like that. Does that make sense?
Sorry we can talk later though.
That's it the question is how many people are using it or who's using it?
Yeah I mean we have a few yeah Nissan using it that's all public the fielder kind of bigger players
so using it the the site level one. We've only recently it's quite hard to track right so it's more
kind of like any open source projects trying to you know gauge those stories and understand
what how people find it useful so yeah times up but I'll be around and you know can talk more
definitely thank you everyone