Capitalizing on Volatility: Smarter Forecasts for a Smarter Energy Market

Presented by: Meteomatics and Yes Energy

Date: July 1, 2025

Duration: 56 minutes

Participants:

• Meteomatics experts: Rob Hutchinson (Energy & Utilities Lead), Chris Rees (Partnerships Manager)
• Yes Energy: Asterios Moschopoulos (Commercial Director) Eilis O'Brien (Senior Forecast Analyst)
• Moderation: Alexander Stauch (Head of Marketing)

This expert-led session explored how short-term weather dynamics and high-resolution forecasts are reshaping power market behavior, spanning real-time demand modeling, price volatility, and trading risk. Speakers from Meteomatics and Yes Energy walked through real-world case studies illustrating how behind-the-meter solar and wind generation, unanticipated renewable events, and misaligned forecasts disrupt grid visibility and distort price formation across Europe and the UK.

As energy-market volatility becomes increasingly weather-driven, the traditional “peak vs. off-peak” paradigm is breaking down. Yes Energy demonstrated how distributed solar and wind are flattening load profiles, such as the collapse of the morning demand peak in Great Britain, and how inaccurate forecasts during low-renewable periods can trigger extreme price spikes, including Germany’s €936/MWh day-ahead on 12 December 2024 and ~€15,000/MWh imbalance prices on 3 June 2024. Meteomatics showed how high-resolution, high-frequency weather modeling, via its 1 km EURO1k model updated hourly, combined with calibrated statistical blends (MOS, ensembles), enables more accurate forecasting of PV and wind ramps, reduces balancing costs, and reveals actionable trading opportunities, particularly during "Hitzeflaute" or "Dunkelflaute" conditions.

The session concluded with a forward-looking panel discussion on the evolving role of AI in weather modeling, the integration of eclipse-aware irradiance corrections, and operational workflows that support the inclusion of curtailments, outages, and customized portfolio parameters.

Key takeaways

• Behind-the-meter solar is rewriting the demand curve
  Great Britain load shape has shifted: the historical morning peak collapses on sunny days as embedded PV bites into grid demand. Modeling that non-linear shape change needs capacity time series plus high-res, frequently updated weather. Meteomatics’ high-frequency, high-resolution inputs let forecasters capture the “solar dip” and avoid nasty surprises.

• Frequent updates = tighter day-ahead
  On 25 May 2025 (GB), the absolute demand low occurred at 15:00, not overnight. Rolling forecasts updated every 6 hours (driven by hourly weather) homed in; the 09:00 D-1 run was ~17 MW from the trough. Meteomatics MOS + hourly refresh cycles cut miss distance as conditions evolve.

• Weather swings move prices, not just megawatts
  Germany’s Dec-2024 cold spell with low wind/solar pushed day-ahead to an 18-year high (~€936/MWh). On 3 Jun 2024, lower-than-forecast radiation/wind plus French export limits spiked imbalance to ~€15,000/MWh in the morning. Higher-fidelity weather signals are pure P&L defense in these regimes.

• Independent signal beats model herding
  EURO1k (1 km, hourly) often diverges from global models on cloud breaks and wind ramps. Even when RMSE gains are “only” marginal, clients saw materially lower balancing costs because the independent signal flags risk where consensus is blind.

• Sub-hour, local detail matters for PV
  The June 3, 2024 “missing solar dip” case showed meaningful spread between models on cloud cover and plant-level PV output. EURO1k picked gaps and reinforcements in cloud fields earlier thanks to hourly cycles, improving intraday positioning.

• …and for wind, including wakes
  In a 25 Jun 2025 southern North Sea thundery ramp, EURO1k captured high/low and timing where a standard EC run missed the event window. Turbine- and farm-level wake parameterization plus LIDAR checks tightened the forecast, a direct lever on imbalance risk.

• Toolbox, not silver bullets: MOS + ensembles + physics
  Yes Energy leans on Meteomatics MOS, ECMWF ENS (50+1) via the API, and ERA5 scenario sets. Meteomatics blends multi-model guidance with physical 1 km runs, giving traders both the “most likely” and the tails they need for scenario-aware decisions.

• AI is useful, but has scope limits (for now)
  Teams are testing AIFS/AI models (e.g., ECMWF, NVIDIA partnerships). They perform well on some variables/time ranges, but short-horizon, asset-scale tasks still favor high-res physics, updated hourly. Meteomatics’ stance: hybrid stack that earns its keep in P&L.

• Operational edge cases are handled, not hand-waved
  Solar eclipses are explicitly modeled via a solar-obscuration variable applied across model streams (ECMWF, UKMO, EURO1k, ensembles). Curtailments/down-regs and site outages can be ingested so portfolio forecasts reflect reality. That’s practical risk control, not theory.

• Market structure is changing — forecasts must, too
  Cooling load sensitivity is rising even in “traditionally less temperature-driven” markets (e.g., Germany). Batteries, heat pumps, and behind-the-meter PV add non-linearities and shorten reaction time. Meteomatics’ high-res, high-frequency forecasts and API-first delivery let shops re-estimate quickly and trade the change, not chase it.

Q&A (topics and concise answers)

1. Can we view combined production of wind and solar—day-ahead or next week—from, say, different providers together?
   Yes. Side-by-side and aggregated comparisons are supported.
2. How do you work with AI-based weather models? Are they more “energy-market ready” than NWP?
   Hybrid approach. Meteomatics runs two NVIDIA-partner AI models, ingests ECMWF AIFS, and experiments with high-res AI. AI is useful (speed, some vars/horizons) but does not consistently beat high-res physics for short-term, asset-level use. Best results come from blending (ensembles/MOS + physics + AI).
3. Does Yes Energy provide demand forecasts for trading portfolios? How does this compare to providers?
   Yes. Yes Energy delivers market-, portfolio-, and asset-level demand (and supply) forecasts across Europe (and other regions), with broad scenario support and fast ops support. They didn’t offer head-to-head claims; emphasized long-tenure client relationships and coverage breadth.
4. Is volatility only from weather/renewables, or also from faster demand response to prices?
   Both, but weather dominates at national scale. Demand flexibility programs exist, yet at whole-market aggregation the effect is modest relative to weather-driven supply swings and unit outages. Tariff design can matter inside retailer portfolios.
5. For >1 MW heat pumps, which market benefits more: day-ahead or intraday? How can forecasts help plan load?
   Data-led answer. Impact depends on metering (behind- vs. in-front-of-meter) and local conditions. Models are re-estimated twice daily and incorporate strong trend/driver terms to detect new loads quickly; use short-horizon high-res weather + ensembles to schedule flexible consumption.
6. Is there a UI to enter site shutdowns so they flow into forecasts?
   Yes. Available today for custom portfolios (and feasible for national wholesale scenarios too). Meteomatics also supports curtailments/down-regulation inputs on the renewables side.