Mapping Renewable Energy Potential: Why Location Changes Everything
Renewable energy manufacturers have a problem. They need to put a number on their product — how many kilowatt-hours a turbine or solar panel will produce per year. But that number depends almost entirely on something they can't control: where you put it.
The result is a landscape of misleading statistics. Solar panels rated under ideal sunshine conditions that rarely occur in practice. Wind turbines marketed with energy projections based on average wind speeds that tell you almost nothing about actual energy yield. The public is left to navigate a fog of optimistic benchmarks that don't account for the single most important variable: location.
This project set out to fix that — by analysing the spatial distribution of renewable energy potential across the entire UK, and giving people honest, location-specific estimates backed by confidence ratings.
The location variable
The UK is a compact country with remarkably diverse weather patterns. The wind energy available from a turbine on the coast of northwest Wales has almost nothing in common with what the same turbine would produce on a rooftop in suburban London. And yet, both installations are typically sold using the same headline figures.
To understand the true picture, we processed millions of hourly weather observations from stations operated by the national meteorological service — wind speed readings from 145 stations and solar irradiance data from 78 stations, each recording every hour of the year. This gave us the granularity to move beyond yearly averages and into probabilistic modelling of what each location actually experiences.
Wind: the high-variance gamble
Wind energy is the more dramatic story. Across the UK, the expected annual energy output from a standardised small-scale turbine ranged from around 600 kWh at the lowest-performing locations to nearly 6,700 kWh at the best — a tenfold difference. The spatial pattern is striking: coastal areas, particularly the west coast of Scotland, Wales, and Cornwall, dominate the high end. Inland areas, especially those with rolling terrain, consistently underperform.
This isn't surprising if you understand the physics. Wind power is proportional to the cube of wind speed — double the wind speed and the available energy increases eightfold. This means small differences in average wind speed translate into enormous differences in energy yield. It also means that gusts matter disproportionately. A location that experiences frequent bursts of high wind will produce significantly more energy than one with a steady but moderate breeze, even if their average wind speeds are identical.
To account for this, we calculated an energy pattern factor at each station — a measure of how much the distribution of wind speeds deviates from the mean. Stations with high variability (fatter-tailed distributions) had pattern factors approaching 3.0, meaning the actual energy available was nearly three times what you'd calculate from the mean speed alone. Stations with more uniform wind speeds had factors closer to 1.6.
Elevation also plays a significant role. Higher stations consistently recorded stronger winds, but attributing those readings to nearby lower-altitude locations would be misleading. We set an elevation threshold to exclude mountaintop stations that weren't representative of where people actually live.
Solar: the predictable performer
Solar energy tells a very different story. The range across the entire UK was remarkably narrow — roughly 1,500 kWh to 2,260 kWh for a standardised panel setup. That's less than a 50% variation from worst to best, compared to the 1,000%+ variation in wind.
The spatial pattern is simpler and more intuitive: a gradual increase from north to south, with the southern coast of England performing best. Almost every location in the UK could produce meaningful energy savings from solar panels. The drop-off only becomes dramatic when you look at the very top of the range.
This consistency makes solar energy far more predictable as an investment. You don't need to be in a special location to benefit — the question is more about degree than viability.
From stations to surfaces
Weather stations give us precise data at specific points, but people need estimates for locations between stations. We used inverse distance weighted interpolation to create continuous energy surfaces across the UK, tuning the parameters to reflect the physical characteristics of each resource.
Wind, being highly localised, was given a tighter interpolation radius based on the denser station network — every point in the UK fell within 78 km of a wind station. Solar, which varies more gradually over distance, used a wider radius of 106 km, matching the sparser but sufficient solar station network.
The power parameter in the interpolation was set to emphasise nearby stations, reflecting the reality that in a country with complex terrain, the closest station is the most relevant predictor. This is especially true for wind, where a valley and a hilltop five kilometres apart can experience completely different conditions.
The confidence question
Producing an estimate is one thing. Knowing how much to trust it is another.
We developed a confidence rating system that considered multiple dimensions of uncertainty. On the network side: how many stations contributed to each estimate, and how closely they agreed with each other. On the data side: the shape of the probability distributions themselves — how fat the tails were, how much variance existed within each station's own readings over the year. A location with a tight, well-behaved distribution from a dense cluster of agreeing stations gets a high confidence score. A location with wide-tailed, volatile readings from a sparse or contradictory network gets a low one.
The results were telling. Solar estimates consistently achieved high confidence on both dimensions — station values across regions showed low standard deviation, and the distributions at individual stations were well-behaved and predictable. Wind confidence was much lower on every measure, particularly along the coast where adjacent stations often reported dramatically different energy levels, and individual stations showed wide, heavy-tailed distributions reflecting the inherently gusty, volatile nature of wind. This isn't a failure of the model — it's an honest reflection of how unpredictable wind is at any given location.
Reporting this uncertainty alongside the estimates was a deliberate choice. The whole point of the project was to move away from single misleading numbers. Replacing a manufacturer's optimistic benchmark with our own overconfident estimate would have been no improvement at all.
What the data actually tells homeowners
For someone in the UK considering renewable energy, the spatial analysis points to clear conclusions:
- Solar panels are a safe bet almost everywhere. The variation is modest, the estimates are high-confidence, and even locations in the north of England produce meaningful energy. The south coast benefits most, but the advantage is incremental rather than transformative.
- Wind turbines are location-dependent to an extreme degree. If you're on the coast or at elevation, the potential is significant. If you're in an inland, low-elevation area — which describes most of suburban England — the expected yield may not justify the investment.
- Coastal areas win on both fronts. The same exposed locations that get strong winds also tend to get more direct sunlight, as weather systems haven't yet formed the cloud cover that develops further inland.
- Manufacturer claims need heavy discounting. Real-world energy yields, even in favourable locations, typically fall well short of marketed figures. The gap is largest for wind turbines in sheltered locations.
Beyond energy: a pattern for spatial estimation
The approach used here — probabilistic analysis of station data, spatial interpolation, and explicit uncertainty quantification — isn't specific to renewable energy. The same methodology applies wherever you need to estimate a continuous variable from a sparse network of observation points: air quality modelling, noise mapping, property value estimation, agricultural yield prediction.
The key insight is always the same: a single number without a confidence rating is worse than no number at all. It gives the illusion of precision where none exists. The most valuable thing a spatial model can do is tell you not just what it thinks the answer is, but how sure it is — and where you'd need more data to be confident.