Energy

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Weather

Renewables

How weather shapes energy production: Why accurate solar & wind forecasting matters more than ever

By Lore Leenknegt

April 16, 2026

4 min read

Across Europe and beyond, weather patterns are shifting. These changes fundamentally reshape how renewable energy behaves. Discover why accurate, weather-driven forecasting has become a core operational capability.

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Across Europe and beyond, weather patterns are shifting. We see brighter springs, more volatile cloud formations, sudden wind ramps, and longer warm periods. These changes affect much more than daily life, they fundamentally reshape how renewable energy behaves.

For energy companies, every sunny hour or unexpected cloud layer directly impacts production, consumption, and market exposure. That's why accurate, weather‑driven forecasting has become a core operational capability for suppliers and BRPs around the world.

This article breaks down how weather data is used, why it matters, and how modern forecasting teams stay ahead.

What weather data do energy companies rely on?

To understand and predict renewable generation, suppliers and BRPs use several key sources.

1. Numerical Weather Predictions (NWPs)

National meteorological institutes publish large‑scale models, such as:

Alaro (Belgium)
ICON (Germany)
Harmonie (Netherlands & Denmark)
AROME, GFS, and many others globally

These models include critical variables:

Solar Surface Radiation (SSR)
Direct & diffuse irradiance
Temperature
Cloud cover
Wind speed & direction
Pressure fields

Different models overlap, differ in resolution, and perform better in some regions than others. So forecasting teams often combine and tune multiple NWPs to match the characteristics of their own portfolio.

In practice, this turns forecasting engineers into weather engineers, translating raw weather data into actionable power output predictions.

2. Satellite imagery for short-term nowcasting

Systems like METEOSAT (Europe) or GOES (Americas) provide:

Infrared imagery
Visible-spectrum imagery
Water vapor maps

This enables nowcasting: predicting solar or wind output minutes to hours ahead based on real-time cloud movement.

Nowcasting is essential for:

detecting sudden cloud shadows
predicting sharp solar ramps
responding to fast-changing wind conditions
adjusting intraday positions proactively

3. Long-term climate trends

Climate change introduces slow but structural shifts:

- earlier solar ramp-ups in spring - more frequent heatwaves - shifts in wind patterns - increased variability across seasons Forecasting models must be retrained regularly to absorb these long-term effects without overreacting to short-term anomalies.

Why weather matters so much in modern energy systems

In traditional systems, production from gas and nuclear units was predictable and centrally dispatched by the national system operator (e.g., Elia in Belgium, TenneT in the Netherlands/Germany, National Grid ESO in the UK, REE in Spain, etc.).

But today's systems rely heavily on decentralized, weather-driven assets:

rooftop PV
utility-scale solar fields
onshore & offshore wind turbines
batteries
heat pumps
flexible demand

Their output depends on the weather and weather is never perfect to predict.

Every day, suppliers must purchase power for the next day on the day-ahead market. To do that effectively, they need accurate predictions for both:

expected consumption
expected renewable generation

If the weather doesn't behave as forecasted:

More sun or wind than expected → oversupply, imbalance exposure
Less sun or wind than expected → shortages, expensive corrections

Intraday markets allow adjustments, but only if forecasting tools detect changes early enough.

This makes high-quality forecasting both a financial lever and a risk management tool.

How energy suppliers handle this and where Amplifino makes the difference

Many system operators provide basic solar and wind forecasts for their national markets. But these models:

are not optimized for individual portfolios
lack transparency
cannot be tuned to local customer characteristics

That's why modern suppliers require tailored forecasting.

Example:

Elindus, a Belgian energy supplier, uses Amplifino's in-house forecasting models to convert radiation and wind fields into expected renewable output tailored to their own portfolio and regional footprint.

Amplifino's AmpliCast™ models combine:

multiple NWPs
satellite nowcasting
local tuning
explainable AI
continuous recalibration

This helps forecasting teams reduce uncertainty specifically in high-impact hours where accuracy matters most.

Even small improvements in forecast quality can result in:

better day‑ahead purchasing
fewer intraday corrections
lower imbalance exposure
more stable operations

Conclusion

Weather will continue to shape the energy landscape across Europe, North America, Asia, and beyond.

Energy players who combine:

strong weather data inputs
advanced, transparent forecasting models
real-world operational experience
tuning to local assets and portfolios

…will be the ones who navigate volatility with confidence.

Amplifino helps forecasting teams turn weather complexity into clear, actionable intelligence.

🌤️ Want to improve your solar or wind forecasting across regions?

Discover our AmpliCast™ forecasting suite or connect with our engineering team to explore a tailored model for your portfolio.

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Lore Leenknegt

Aug 11, 2026

Forecasting renewable energy during a solar eclipse

A solar eclipse is one of the few events you can pinpoint years in advance, down to the minute. Forecasting what it does to solar and wind power is another matter. Because eclipses are so rare, there is little precedent to lean on, which makes them both a real challenge and a genuine chance to learn. That is exactly why we studied this one closely and built its effects into our forecasts, well before the day itself. On August 12, 2026, a solar eclipse will move across parts of Europe. The path of totality runs over the North Atlantic, Iceland, and northern Spain, while a deep partial eclipse will be visible across much of the continent. In many of the markets we forecast for, the Moon will cover a large share of the Sun in the early evening. Over Belgium and the wider region, for example, that means roughly 85 to 90% coverage around 20:12 CEST, with the Sun already sitting low in the western sky. For most people it's a striking thing to watch (with proper eye protection). For a team that forecasts renewable energy for a living, it's something else entirely: a rare, natural stress test for the models we rely on every day. Here is why this matters for forecasting, and how we prepared for it. Renewable forecasts live and die by weather parameters Solar and wind generation forecasts are built on top of Numerical Weather Prediction (NWP) models. A handful of variables do most of the heavy lifting: SSR (Surface Solar Radiation): the total solar energy reaching the ground. DNI (Direct Normal Irradiance): direct sunlight hitting a surface pointed straight at the Sun. DHI (Diffuse Horizontal Irradiance): sunlight scattered by the atmosphere before it reaches the surface. Surface temperature. Wind speed and wind direction. Get these right, and a solar or wind forecast has a solid foundation. An eclipse briefly disturbs almost all of them at once, which is exactly why it deserves attention. What an eclipse does to the atmosphere During an eclipse, the atmosphere suddenly loses part of its primary energy source: incoming sunlight. It is a bit like a sunset, except the drop is far more abrupt, and then it reverses just as quickly. The measurable effects are well documented: Solar irradiance falls sharply. Surface temperatures can dip by roughly 1 to 6 degrees Celsius. Wind speeds may briefly ease, and local wind directions can shift as the lower atmosphere becomes more stable. None of this is dramatic in isolation. Taken together, and compressed into a short window, it is a pattern that rarely shows up in normal weather data, so it is worth handling deliberately. Making sure the models see it Capturing an eclipse correctly is not automatic for every forecasting setup. A weather model only reflects the event if its radiation calculation is told that the Sun is being progressively blocked, and by how much, at each moment. Not every operational NWP model does this on its own. That is precisely the kind of detail we account for in advance. Part of preparing for August 12 is making sure the reduced solar input is represented for the short window when it actually matters, rather than letting the forecast treat it as an ordinary evening. Knowing which inputs handle the eclipse, and correcting for the ones that do not, is what keeps a forecast reliable when conditions step outside the everyday. So what does it mean for forecasts that day? The honest answer: the overall impact should be modest, and that is mostly down to timing. This eclipse peaks in the evening, shortly before sunset, when solar production is already in the steep, declining tail of the day. Output is low and falling fast anyway, so removing part of an already small number has a limited effect on total generation. Because it happens during that evening transition, the impact on wind generation is expected to be limited too. ![Solar eclipse forecast impact chart](/assets/images/blog/blog_5_inline.webp) For traders and system operators, the market impact should therefore stay contained. The more interesting challenge is not the drop in generation itself. It is the unusually rapid ramp down and ramp back up of solar power around the event. Sudden swings like that can create frequency control challenges for grids carrying a lot of photovoltaics. Had this eclipse fallen around noon, with the Sun high and solar output near its peak, the operational story would have been very different. Prepared, not surprised None of this is left to chance on the day. Because an eclipse is fully predictable, we studied this one ahead of time and made sure its effects are reflected in the forecasts our clients rely on across European markets. That said, we are genuinely curious to see how the real world compares to what we prepared for. Rare events like this are a valuable check: they confirm what our models already handle well and highlight the details worth sharpening for next time. Forecasting is never finished, and moments like this are exactly why we keep refining it. References Harrison & Gray (2017), *The weather's response to a solar eclipse. [academic.oup.com](https://academic.oup.com/astrogeo/article/58/4/4.11/3988889) Aplin et al. (2016), *Atmospheric changes from solar eclipses. [arxiv.org](https://arxiv.org/abs/1603.02987) Palomaki et al. (2019), *Observations of thermally-driven winds during the 2017 solar eclipse. [mdpi.com](https://www.mdpi.com/2073-4433/10/7/389) Recent studies on eclipse impacts on temperature, humidity, and wind fields. [mdpi.com](https://www.mdpi.com/2073-4433/16/7/872)

Amplifino joins De Blauwe Cluster: bringing data-powered forecasting to the blue economy

The energy transition doesn't stop at the shoreline. As offshore wind continues to grow as a cornerstone of Belgium's renewable energy mix, the complexity of managing, forecasting, and optimizing that generation is growing right along with it. That's exactly why we're excited to announce that Amplifino has officially joined [De Blauwe Cluster](https://www.blauwecluster.be/), Flanders' leading network for the blue economy. A Meaningful Milestone On June 2, our director Rik Deruyter took the stage at the "Navigating the North Sea" event in Ostend to introduce Amplifino as a new member. The timing couldn't have been better: Minister of the North Sea Annelies Verlinden was also present to unveil Belgium's new Marine Spatial Plan (2026–2034), a strategic framework that underscores just how seriously the sector is taking the long-term development of our North Sea. For us, joining this ecosystem isn't just a formality. It's a commitment to being part of the solution. What we bring to the table Offshore wind is one of the most data-intensive and operationally demanding segments of the energy transition. Generation profiles are highly variable, weather dynamics offshore are complex, and the stakes, from imbalance costs to grid stability, are real. At Amplifino, we specialize in exactly this kind of challenge. Our AmpliCast platform delivers high-precision solar and wind generation forecasts, built on advanced machine learning models, real-time data pipelines, and explainable AI. That last part matters: every insight we deliver is transparent and traceable, so your team can act on it with confidence. Within the De Blauwe Cluster ecosystem, we contribute our expertise in: Data collection and processing for offshore and nearshore energy assets Advanced wind energy forecasting, including day-ahead and intraday predictions Digital sea initiatives, supporting smarter, data-driven decision-making across the blue economy Looking Ahead The blue economy is at an inflection point. New offshore projects are being planned, the Marine Spatial Plan is setting the boundaries, and the pressure to make every megawatt count is higher than ever. We're here to make sure the data side of that equation is handled with the rigor and precision it deserves. We look forward to collaborating with the innovators, researchers, and industry leaders in De Blauwe Cluster and to building something genuinely useful together. Data-powered progress. Now navigating the North Sea too. Want to learn how Amplifino's forecasting solutions can support your offshore energy projects? [get in touch](/contact) and we'd love to show you what's possible.

Why you can be "right on average" and still pay imbalance costs

Understanding the hidden covariance effect in power markets Forecasting teams often optimize for a simple goal: be "right on average". Near‑zero mean error? Check. Near‑zero imbalance–day‑ahead spread? Check. So why does the imbalance bill still come in positive? Here's the part many market participants overlook: Your bill isn't driven by the average of two numbers — it's driven by the average of their product. In other words: Imbalance cost ≈ forecast error × (imbalance price – day‑ahead price) Over time, that becomes: mean(imbalance cost) = mean(error × spread) = mean(error) × mean(spread) + covariance(error, spread) This last term "covariance" is the silent force behind persistent imbalance costs. Even if your average error is close to zero, and your average spread is close to zero, a positive covariance will still generate a positive bill. Why covariance matters in real power markets Power markets aren't linear. They're full of stress moments: scarcity hours, renewable volatility, plant outages, grid events, and unexpected ramps. These are also the exact periods when: Forecast errors tend to be largest Imbalance spreads widen dramatically BRPs, traders, and asset owners feel the most financial pain So the system doesn't penalize you because you're "wrong on average". You're penalized because you're wrong at the wrong time. That alignment — large errors occurring in high‑spread hours — is exactly what pushes the covariance term positive… and keeps the imbalance bill stubbornly above zero. The real takeaway: optimize for the moments that matter Reducing bias is not enough. Improving your average error is not enough. What matters is reducing error when spreads explode. That's where true financial impact lives. In practice, this means focusing forecasting improvements on stress regimes: Highly volatile solar and wind periods Transition moments between renewable ramping Scarcity events and system imbalances Grid congested hours Weather‑driven uncertainty windows When you improve accuracy specifically during these high‑spread hours, you directly reduce covariance — and with it, your structural imbalance cost. In one sentence You don't lose because you're wrong on average — you lose because you're wrong at the wrong time. The Amplifino perspective At Amplifino, we see this pattern across the portfolios we help forecast and optimize. The winning approach combines: High‑performance forecasting models tuned for volatility Explainable AI that reveals where and when errors spike Stress‑regime modeling to identify high‑spread conditions early Continuous recalibration to ensure models stay stable in extreme situations Deep operational knowledge of BRP processes and market behavior Our goal is simple: ensure energy companies are not just "balanced on average", but balanced when it matters most. Want to reduce your imbalance costs where it actually counts? Talk to our forecasting engineers. We help BRPs, traders, and asset owners build forecasting systems that perform exactly during the most financially impactful moments. Explore AmpliCast™ or request a technical session with our team.

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