Yes, hello. I am Sol, this is Alex. We're not Peter Durfield. Sorry about that. You
couldn't do it, so we've got, you've got us instead. We'll do our best. And yeah, we're
going to be talking to you today about our open source global solar forecast and dashboard.
I'm going to do it in this order. I'll talk a bit about who we are and why we think we
can do this and then I will hand over to my farmer talented colleague who tell you about
the actual meat of the matter. So, without further ado, who are we? Well, we're two members
of open plan at fix. We're a non-profit open source startup and we work primarily on renewable
energy forecasting. So we do solar forecasts. We do wind forecasts. We do cloud forecasts.
You say cloud forecasts, they're not renewable energy. I know, but we forecast them anyway,
because we love it. We love forecast them so much. It is actually useful, I promise. Yeah, we'll
come to that. And we do this to try and reduce emissions in the UK, see our two emissions. And
how do we reduce emissions? Why do we do solar forecasting? Why does that help? Well, the idea
is solar is growing, right? It's going to be the biggest, the most used form of electricity
generation by 2040. It's huge. We love to see it. But there's a problem with solar. We know
it. It's fickle, right? It's the clouds of there. It doesn't work as well. If there's pollution
in your atmosphere, it doesn't work as well. If your panel is on upside down, it doesn't work at all.
At night time, it goes away, right? It's hard. So, we thought that there was something
something there where we could save a lot of CO2 by basically making it more accurate,
because imagine if you're running a grid and your grid is made up of mostly solar. It's 2040.
We're living in the future. And there's a big error bar between your generation
that you think you have and what you actually have, because the solar is fickle like I said.
It makes it hard to balance your grid, but it's a man's spike that you're trying to match.
It doesn't line up with the generation that you have. You get a blackout. So, that's what
happens here in the UK. We basically have these big gas turbines, and they are always running.
They're always releasing CO2, and they can be turned up when the solar isn't quite what we
thought it would be. And that's bad for the environment. So, we say, okay, if we can provide a really
accurate solar forecast, then we can tell that the grid operations, there's going to be this much
at this time. They can line it all up, and they can turn these off altogether. And that saves CO2.
Which is brilliant. And so, that's what we did. We built a model PVNet. We trained it on
a load of data, and we provide that to the national grid, and we have this looking forecast,
and they use that in the operation room, and it's quite out to tell how well it's working,
because there's a lot of decisions in the tree, but we think we're saving a lot of CO2 already in the UK,
and they seem to like it. So, that all works well. Obviously, this is an open source conference.
We want to make this kind of utility of solar forecast available to as many people as we can.
So, how do we take something like PVNet, which is big, which is kind of run as a service.
It's hard to contribute to. How do we take that and how do you make it more readily available?
Less UK centric, more contributing, contribute to friendly. And that's where we thought the idea
of the global solar forecast, which is what Alex will not tell you about.
Thank you. Thank you. Thank you.
Yes, so something so has already touched upon. We have something that works quite well for the
UK, but it is quite bulky. So, the dream would be to provide accurate solar forecasts for
everybody everywhere, who ever needs it. If you want to forecast for your solar panel at home,
to know when to charge your car, if you want to run an energy grid somewhere else, that is not the UK,
which is a very normal thing to do. And obviously, there's 14 of us. We can't do it alone.
So, the obvious solution was to take an open source and hopefully get people to contribute,
and then we can together build the beautiful solar bunker future that will eventually arrive, hopefully.
I'm not going to go into the whole, why open source is great to build. I know which gone through
it and time. So, it started with us creating open courts, which is the lightweight version of what
we're doing in the UK. It's a smaller ML model. It just runs on three open weather forecasts.
It's wrapped into a Python library. You can download it and run out of your laptop, just tell it,
something about assets. As advertised in full lines of code, you might have seen this picture before,
because we've presented open courts in 2024 at Foster. So, maybe some of you have been here.
And initially, oh, this doesn't read on this projector at all, but this is a map. I promise.
Yeah, eventually it was a replacement for the UK service, so an open UK service.
But people started using it in other countries and telling us about it and we have the tracker
of little flags when people tell us about them using it somewhere else on a GitHub page.
And we decided to put like a wrapper around it to take a global as well.
Yeah, so this is what the global forecast is. It takes centerpoints for the each country.
It gets the capacity for it. It produces forecast 48 hours ahead for solar power in the country.
And there is a beautiful yacht on top of it, which is quite fun to explore,
which we are going to do now. Come along and let's see if we can crash that thing.
Maybe we did.
Yeah, my bad.
Yeah, okay, maybe not a demo today. That's very sad. I should have done that beforehand.
Okay, well, it's waking up. Let me talk about how we expected to be used and how you can get involved.
So again, obviously we're compared by ourselves, and this is very much a work in progress.
It's under the Herded Runs Open Quartz, which has been trained on UK side data.
So basically solar roofs of panels, a generation from that. So it might not perform as well in different
geographies. And we also don't really know how it's doing in different geographies. We have some
benchmarking for the UK, but not for anywhere else. And yeah, so if somebody knows of other
generation data that we can get access to to train a model wider, or wants to train a different
model. You can swap them in and out in the open Quartz. It was to help us point benchmark it.
Just if you use it, please tell us. If you don't, and it's because there is some feature that
you would like us to have. Also please tell us, maybe we'll add it. Maybe it's a bit works now.
Oh boy. I do apologize.
Hello. Hello. Is it the wig? Yay! Okay. So no. It takes a bit to focus for the entire world. Please forgive
this. But I don't think we'll maybe have time to have it a little look, but if you do get time
in your free time, it does look kind of cool. Please take a look. I'm going to buy him this morning.
My mum. Hmm. Yeah. Maybe I can tell you a white cloud forecast thing is useful,
well we're waiting for this to load. We use satellite imagery in our model. It tends to tell you
quite well. Let's tell you a lot about where the cloud's are, and that obviously helps with solar energy.
But unfortunately, you can only have satellite data in the past. So we're starting forecasting
satellite data into the future to see where the cloud is going to be in the future. Okay.
Speedy runs through. So we have this fun chart. Okay. So this is the solar generation for the
entire world. And you can see who's contributing the most at any given point.
Yeah. I was going to do a pop quiz to ask which country contributes this giant police
bikes, but it's obviously China no prices for that one. Yeah. Fun fact about this. This is
15 minute resolution and like at peak there is enough solar power forecast like produced in the
entire world to power the entire world for 15 minutes, which is nice. And then this is what we
had a sneak peak of before. So this is how much it is for gas surge in each country and we can normalize
by the country's capacity if I can hit the button. Yes. So instead of gigawatts, you can see
like the percentage of the assets that are utilized. Yeah, which is a lot easier to see for
different countries on the same scale and you can also see where the sun is at a given point.
Yeah. You can click into a country, have a forecast for that one specifically.
Oh thank God it works. And also see the capacities for the world.
Oh dictionary capacity. Yeah. There is this nice table. We've added maybe 20 or 40 of these.
The arrest was done by the open source contributors and also somebody transformed our
very laying to the map into the globe that you've just seen, which was very cool.
And there is some documentation. I don't know how to work a Mac. Please forgive me.
That you could have read instead of listening to me. But I can drive this as well.
And illustrate a guide. Okay. So hopefully I can go.
Yeah. Okay. Thank you very much for listening. Sorry for some technical difficulties. Please find
this. Please connect with us. Please contribute to us. Thank you for having us.
Questions?
Nice. I'm lucky. It's trying to work with the API. And like he, so book he asks, I'm who to talk to
about the problems talking to the fraud solar API. Like is trying to access the API?
Okay. And has some errors and wants to. Okay. Well I didn't think I can debug this in real
time, but do email us. There is a support. Cool. Yeah. It should be there should be an email in the
docs. Are great operators using this in production? And it's the UK TISO. Right?
Yes. So our main model, Pivenet, is a bit of a beast. So we run a service in real time for the UK
National Grid. They do use it in the control room to have an idea of how much thought is
going to be forecast. What is going to be there in the country? It is definitely used.
Open Quartz. I think we forecast for a couple of people as well with like solar farms.
Yeah. I hope that answers this. Cool. Oh, I have a hand there.
Do you just briefly about cloud? How did you come up with a few people?
Uh, the question was, uh, can we can I tell them, can I tell everyone around about clouds and what
the granularity of the forecast is? I will not remember the granularity, uh, spatially off the top of
my head. Uh, temporally, I think it's five minutes late, maybe 15 minutes late, depending on the
service that is used in the model. I'm sorry. This is not my project. It's based on the
you met sat, uh, mess your sat data. So whatever the granularity that is in terms of spatial resolution.
Yeah. Yeah. Um, is there a follow-up? I don't know if I talked enough. Okay. Please.
Yeah. So the question was, uh, in Australia, there is a system that uses rooftop solar, uh,
generation data in real time to see if there's clouds in the neighbourhoods and then it's
moving towards you if I understand this correctly. Yeah. Uh, and if we're doing something similar
or thinking of doing something similar. So yeah, uh, actually a my journey at OCS started with,
a master's project that did something similar. We tried to, like, get the solar farms, like,
uh, get the data from neighbouring solar sites. Uh, to contribute to the model, the problem is,
uh, in the alive service, you can't really rely on the quality of data, uh,
unfortunately in the UK and, like, the, uh, it got out. No, I have a bit of reports. So, yeah. And in
Pivenet and our main model, we don't actually put in live data because of that reason. There's just
not, um, there isn't a reliable stream. We've thought about sky crammers on and off, but then
never got around to it. Yes, please. Sorry. What was the, uh,
what was coming up? Yeah. So, yeah, we've had people talk to us about how
focus and issue. We're not doing anything, uh, folk related at the moment, as far as I know,
there's always too much to do, but it's kind of on the list somewhere. We take, um, we take
from the end of repeat later, we take a visibility parameter and that has some of the fog.
Yeah. You know, in co-related, but not in terms of satisfying. Yeah. So it goes into the model as,
like, a data stream, hopefully, um, like, a variable that captures it.
Time's up. I'm told. Thank you very much for having us.