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

By Joost Bruneel

February 3, 2026

3 min read

Forecasting teams often optimize to be "right on average". But your imbalance bill isn't driven by the average of two numbers — it's driven by the average of their product. Discover the hidden covariance effect in power markets.

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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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Robin Bruneel

Apr 16, 2026

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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