(Approx.) Great Britain Electricity Generation charts

Since I got back to the UK, I’ve been trying to understand and take more of an interest in what’s going on here again (although unfortunately, that’s often somewhat depressing across the political spectrum), and one area I’ve been looking into is Electricity generation (and prices!) for Great Britain.

There’s a large amount of renewable electricity generation from Solar and Wind these days, but it’s obviously highly variable with the weather, the time of year (i.e. day length for Solar) and cloud cover, so Natural Gas generation is still used to “top up” demand (often to very significant proportions), but these days Coal and Oil don’t appear to be used any more (Gas is the cleaner alternative and can be spooled up very quickly when needed).

There are a number of nice live dashboards people have made showing the data summary - National Grid: Live and Live GB Electricity Generation being two good ones, and there are free data feeds from a variety of different sources (although some of the values are estimates for certain things).

Very few of the existing dashboard sites appear to have detailed long-term historical chart data - they seem to either approximate it with daily averages further back in time, or just limit the history duration - and the fidelity of some of the charts is often quite limited due to filtering (somewhat understandably given some of the limitations of the source data), so I decided to try setting up a data storage and chart generation setup to scratch this itch for myself.

Stacked area chart:

Line plot chart:

Given I also wanted high-fidelity charts (non-interactive for the moment), I decided to use Python for this given how nice matplotlib charts are (especially when using seaborn styling), as I had also been meaning to dust some of the cobwebs off my Python skills that I hadn’t seriously used for almost two years, and I was similarly keen to play around with some of the newer Python tooling like uv, which is a more recent Python package and project management tool, which is written in Rust and is much faster than PIP.

There’s no single central source for a complete breakdown of electricity generation source data in Great Britain - the closest to that seems to be Elexon who provide regular snapshots (every 5 minutes) via data APIs of various different aspects of electricity generation and interconnect transfers, however there’s no single API that returns all generation sources, and some data sets are non-complete, in that they don’t include full Wind power numbers and don’t have any Solar power numbers. There are forecast APIs which estimate these numbers more accurately, but they’re only updated every 30 mins, so in the end I decided to use a different source for the Solar (PV) generation which is Sheffield Solar which is also an estimate, but seems to be updated more regularly.

For the moment, I’m using those two data sources for all generation types - note however this does mean that the numbers for Wind generation are lower (often by around 15%-20% I think) than the actual true Wind generation values, as the source data doesn’t included “embedded” wind farms. I’m also not including any of the Interconnector transfer amounts (which can be positive or negative depending on the direction of transfer) to neighbouring countries, which means the charts aren’t an accurate display of Demand of electricity generation in Great Britain.

So I now have an application setup where data is gathered every 5 minutes from the data source APIs, stored in an SQLite DB, and then I can query the data from a FastAPI-implemented API, which is then fed into matplotlib to plot the above charts locally. The server is running in a Podman container.

I think the stacked area chart looks nice, but obviously suffers from the typical read-ability issues stacked charts can suffer from in terms of gauging absolute magnitude, especially when offsets to lower data series happen significantly in a short space of time, and distort or squeeze the rest of the chart, but it does allow to roughly see overall trends during the day and through a week.

I am tempted to in the future try and additionally store some weather data (temperature and wind speed) for a variety of different locations around the country to see if plotting those against the Generation values (Demand values would arguably be better) would show likely correlations, i.e. more electricity use when it’s colder for heating (probably not that much AC in the UK) and more wind generation when it’s windier.

I’d also like to play around a bit with interactive JS/Canvas charts and see if it’s possible to have something that’s close to the presentation and fidelity of the matplotlib charts.