Flux50 Connect 2026: What Belgium's grid debate tells us about the future of forecasting

By Reinout Daels

May 26, 2026

3 min read

Amplifino's Product Manager Reinout Daels attended Flux50 Connect last week 21th of May 2026 . What struck him most wasn't surprise, it was recognition. The sector has caught up to the problems we've been solving.

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The energy sector doesn't always move fast on acknowledging hard truths. So when an entire room of Belgian grid operators, flexibility providers, EMS builders and asset managers spends a full day reaching the same conclusions, the convergence is hard to ignore. Our Product Manager Reinout Daels attended Flux50 Connect last week, and what struck him most wasn't surprise. It was recognition.

The "sunny Sunday" problem is now a structural market condition

At Amplifino, we've been tracking incompressibility risk in our forecasting models for a while. The signal has been getting louder. What Flux50 confirmed is that the broader sector now sees it the same way.

Easter Monday 2026. April 26. May 1 and several other public holidays this spring. Belgium hit extreme negative imbalance prices repeatedly and the DA floor price had to be adjusted from -500 to -600 €/MWh because the existing limit kept getting breached. Elia was transparent about it at the event: this is a recognized strategic problem, and the Summer Outlook 2026 is essentially a public acknowledgment that uncovered flexibility still exists in the system.

For anyone running renewable portfolios or managing BRP positions, none of this is abstract. These events show up directly in your P&L. The question has shifted from "could this happen?" to "how often, and are you ready when it does?"

Proactive forecasting is the difference between capturing value and watching it pass

One case study showcased on Flux50 Connect illustrated this cleanly. A flexibility provider had already forecast negative prices for solar hours before the market cleared and positioned their assets accordingly. When reactive curtailment by others tightened supply and flipped prices positive, they de-curtailed and captured the upside. Everyone else was responding to something that had already happened.

This is exactly the dynamic we build for at Amplifino. The value of a forecast isn't just accuracy in stable conditions, it's being right at the moments of highest volatility, and having that signal integrated tightly enough into operations that you can actually act on it. That gap between insight and action is where money is made or lost.

Congestion is moving from background risk to active market

Fluvius walked through the mechanics of grid congestion with a message that's becoming harder to ignore: electrification is accelerating fast enough that DSO-level flexibility is no longer a nice-to-have. Platforms like NODES are opening up commercial congestion markets, and the regulatory direction from Brussels, network codes, demand response frameworks, grid investment packages, reinforces that this trend has structural legs.

The companies building forecasting and optimization capabilities now are the ones that will be well-positioned as these markets mature. The ones waiting for the market to fully develop before investing tend to find they're already behind.

What the room confirmed

Flux50 wasn't a wake-up call for us, it was a mirror. The challenges that came up throughout the day are the same ones we work on with our clients every week: volatile imbalance prices, the operational complexity of acting on forecasts in real time, the need for models that handle extreme market conditions as well as average ones.

It's useful when an entire sector event lands on the same page. It means the conversation has moved past "is this a real problem?" and into "what are you doing about it?", which is exactly where we like to operate.

If you're rethinking your forecasting setup in light of what's happening in Belgian and European energy markets, get in touch with us and we'd be glad to have that conversation.

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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.

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

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. 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, and 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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