The €2 billion blind spot: why your energy forecasts are costing you money

In the energy market, precision is profit. Every day, renewable energy operators commit to delivering a certain amount of electricity to the grid. But what happens when the wind doesn't blow as hard as predicted, or when clouds unexpectedly roll in? You fail to meet your commitment. And that failure comes with a hefty price tag.

This is the world of balancing costs. When supply doesn't meet demand, the grid operator has to step in to "balance" the system, usually by firing up expensive, fast-reacting power plants. The cost of this intervention is passed on to those who caused the imbalance. For energy producers, this amounts to a penalty that can range from €1 to €5 for every megawatt-hour (MWh) they produce.

It might not sound like much, but it adds up. Across Europe, these costs run into the billions of euros annually. And here's the critical part: studies show that 10% to 30% of these balancing costs are a direct result of inaccurate weather forecasts. You're paying a tax on bad data.

Why standard forecasts fall short in the energy market

The problem isn't that weather forecasts are "wrong." It's that the tools many operators rely on are too coarse for the job. Global models from providers like ECMWF or GFS are incredible feats of science, but they see the world in 9 to 25-kilometer pixels.

Imagine your wind farm is located in a complex terrain with hills and valleys. A global model will provide a single, averaged wind speed for the entire 25 km grid box. It completely misses the local speed-up effects over a ridge or the calm air in a valley. It can't resolve the fine line of a sea breeze front that might shut down your coastal turbines for an hour.

Similarly for solar, a global model can't distinguish between a thin layer of high-altitude cirrus clouds that barely affects your output and a thick, convective storm cloud that will cause a near-total drop in production. It sees both as just "cloudiness."

This lack of detail leads to forecast errors. And in a market that operates on tight margins, these errors are not just academic; they are a direct hit to your revenue. You are consistently either over-predicting and paying imbalance penalties, or under-predicting and missing out on the opportunity to sell your full potential output at the best price.

High-resolution models: a tool for competitive advantage

This is not a problem you just have to accept. The solution is to use a sharper tool. High-resolution numerical weather prediction (NWP) models, operating at scales of 2 kilometers down to a few hundred meters, are designed to capture the local weather phenomena that global models miss.

By simulating the atmosphere in much finer detail, they can:

• Resolve terrain effects: A 400-meter resolution model understands the shape of the hill your wind turbine sits on. It can accurately predict how wind flows over and around it.
• Capture convection: Instead of just predicting a "chance of thunderstorms," these models can simulate the lifecycle of individual storms, giving you a much clearer picture of when and where heavy cloud cover and gusty winds will occur.
• Predict boundary layers: They can accurately model small-scale features like sea breezes or fog formation that have a huge impact on both wind and solar generation.

The result is a forecast that is fundamentally more accurate. At Weatherwise, we've seen that using kilometer-scale models can reduce energy forecast errors by 10% to 30%. That's not an incremental improvement. It's a direct reduction of the "hidden tax" you pay on every megawatt-hour.

Beyond energy: reinsurance and pricing climate risk

The same principle of precision applies directly to the insurance and reinsurance industries. For decades, insurers have priced risk based on historical data and regional climate models. But as weather patterns become more volatile, this approach is becoming less reliable.

Consider a hailstorm. A traditional forecast might identify a county-wide risk. A mesoscale model can provide probabilities of 2-centimeter hail impacting a specific group of zip codes. This allows an insurer to:

• Price policies with surgical precision: A homeowner on a hill exposed to wind may carry a different risk than one in a sheltered valley a kilometer away.
• Manage portfolio risk: Understand the correlated risk of a single weather event impacting thousands of policies in a concentrated area.
• Innovate with parametric insurance: Develop new insurance products that pay out automatically based on verified weather data (e.g., if wind speeds exceed X at a specific location), rather than a lengthy claims process.

In a competitive market, access to better data is a key differentiator. While your competitors are using a blurry map, you can navigate with a high-resolution satellite image. You can anticipate market movements, optimize your assets, and price risk more intelligently. The €2 billion blind spot isn't a force of nature; it's an information gap. And today, we have the technology to fill it.