An experimental tool transforming the way energy is understood
In response to the energy crisis in the UK, our London team created an experimental energy forecasting tool.
With COP26 on the horizon in the UK, climate change on our minds, and the need for a equitable, fair, and quick green revolution, we decided to make it part of our mission to try and understand energy better. This project came out of that effort.
Energy Proverb Net is a portal into an alternative reality where we have a deeper connection with our energy systems. Using what we have available today, it imagines a new relationship with energy systems.
Focussing on the British Isles, the experiment provides a map-based energy forecast experience not unlike the shipping forecast which has been used for decades to describe the weather at sea.
Photons fall, panels aglow, to the town, energy will flow.
The experimental weather forecasting tool provides forecasts in the form of proverbs to help make it easier to recall how changing weather patterns may locally effect renewable availability. These proverbs are generated using artificial intelligence.
Proverbs are spoken, making it easier to consume consciously. Understanding how weather relates to local renewable energy availability and being able to remember what kinds of weather patterns result in low carbon intensity makes it easier to make informed descisions about energy use.
Creating Energy Regions
We began the project by quickly mapping every power plant in the United Kingdom and Ireland. Data for this is readily available. In our case, we used a combination of datasets from Elexon and the World Resources Institute. With a bit of cleaning in R and a quick export via ggplot2, we were able to quickly see just how many power sources feed into the grid of the British Isles.
Pulling the data into QGIS, we were then able to create energy regions for the British isles. These regions are created through a clustering algorithm and then shaped and scaled based on a region's total capacity.
At the current moment, these are basic energy clusters - but in the future we'd like to explore how energy regions are weighted by local population density and actual recored energy demand.
Artifical Intelligence closes the gap between data and poetry
