Europe Renewable Forecast API: How EPT-2 Beats ECMWF

Written by: Olivier Lam, Physical AI Team, Jua.ai AG

Key Takeaways for European Renewable Traders

• A Europe renewable forecast API must deliver accuracy, refresh frequency, integration ease, and hindcast availability to support wind and solar trading.
• EPT-2 outperforms ECMWF HRES on 100 m wind, 10 m wind, temperature, and solar radiation across the full 0–240 hour lead-time range, with benchmarks run on 10,000+ real ground stations.
• EPT2-RR provides up to 24 daily refreshes, six times more frequent than traditional NWP, so intraday trading decisions use forecasts less than one hour old.
• The Jua Python SDK, Apache Arrow support, native ENTSO-E integration, and multi-model hindcast access remove the integration tax quant teams face when stitching together separate data sources.
• Run an EPT-2 benchmark with Jua to compare against your current provider in under five minutes.

The Shift Toward Physics Foundation Models for European Power Markets

European power markets are structurally exposed to weather. Wind and solar now account for a growing share of installed generation capacity across Germany, Great Britain, France, the Netherlands, and Belgium. Forecast error on a 1 GW wind portfolio translates directly into imbalance costs and hedging losses. A four-percentage-point accuracy gain on a 1 GW wind portfolio saves approximately €1.5 million per year in European energy markets. The equivalent gain on a 1 GW solar portfolio saves approximately €3 million per year, and the impact scales with portfolio size.

The forecasting infrastructure behind these markets was designed for a different era. Numerical weather prediction (NWP), which decomposes the atmosphere into three-dimensional grid cells and solves differential equations inside each one, has led the field for forty years. A single NWP simulation consumes approximately 8,400 kWh and costs €1,000–€20,000 to run on high-performance computing infrastructure. That compute cost caps update frequency at two to four runs per day, which has constrained the energy industry since the method was invented.

General physics foundation models remove that constraint. These spatiotemporal transformers train on observational data and learn the governing conservation laws of physical systems, such as mass, momentum, and energy, in a latent representation that integrates forward in time faster than the physics itself unfolds. A single inference on such a model runs on a single GPU in minutes at approximately 0.25 kWh and $0.20–$15. That cost asymmetry, roughly four orders of magnitude cheaper than NWP at run time, makes 24 daily refreshes economically viable.

Jua builds these foundation models for reality and the agent that operates inside it. The Earth Physics Transformer (EPT) family is Jua’s general physics foundation model, and Athena is Jua’s AI agent. Jua for Energy is the first applied product built on both.

Run a live EPT-2 benchmark against your current provider to see the four-order-of-magnitude cost advantage in action.

How Current Providers Serve European Renewable Forecasting

The Europe renewable forecast API market divides into four main categories. ENTSO-E (the European Network of Transmission System Operators for Electricity) publishes actual generation and capacity data via a public API, which provides grid data rather than forecasts. Solcast is a point-solution SaaS vendor specialising in solar irradiance and rooftop PV forecasts, built on processed NWP outputs. Electricity Maps provides carbon intensity and real-time electricity generation data by zone, oriented toward sustainability reporting instead of trading-grade forecast accuracy. Open-Meteo is a free, open-source NWP aggregator that exposes ECMWF, GFS, ICON, and other model outputs via a REST API without ensembles, hindcasts, or a productised refresh schedule. Jua for Energy is a production-grade workspace that combines EPT-2 physics-constrained forecasts, a 25-model benchmarking surface, Athena-driven workflow automation, and native ENTSO-E integration under a single API schema.

Core Forecasting Concepts for European Power Trading

NWP (numerical weather prediction) solves atmospheric differential equations on a discrete grid. RMSE (root mean square error) measures the average magnitude of forecast error against observed values, and lower values indicate better performance. CRPS (continuous ranked probability score) measures the skill of a probabilistic forecast, or ensemble, against a single observed outcome, and lower values again indicate better performance. An ensemble is a set of forecast members initialised with perturbed conditions to represent forecast uncertainty, and the spread of members encodes probabilistic skill.

Lead time is the interval between forecast issuance and the valid time of the prediction. A hindcast, or reforecast, is a forecast generated retrospectively over a historical period and used to backtest trading strategies against ground truth. Any-Δt forecasting means the model is trained to predict at arbitrary time steps rather than rolling forward in fixed increments. EPT-2 does not roll forward in 6-hour steps the way Aurora and most NWP peers do, which avoids the error compounding that fixed-step roll-forward introduces.

Evaluation Framework: Four Dimensions for API Selection

With those technical concepts in place, the next step is to evaluate how providers perform on the four dimensions that determine whether a forecast API is production-ready for European renewable trading.

1. Accuracy on wind and solar variables. The relevant variables for European renewable trading are 100 m wind speed (turbine hub height), 10 m wind speed (surface reference), and surface solar radiation downwards (SSRD). As noted in the key takeaways, EPT-2’s accuracy advantage over ECMWF HRES holds across all renewable-relevant variables, documented in arXiv:2507.09703 with benchmarks against more than 10,000 real ground stations. EPT-2e, the ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time. Benchmarks run via open-source StationBench with no post-processing or station fine-tuning. Microsoft Aurora has no SSRD output, which removes it from solar forecast comparisons by default.

2. Operational refresh frequency. Day-ahead and intraday trading windows require forecasts that update faster than the 2–4 daily NWP runs the industry has historically received. EPT2-RR updates up to 24 times per day. EPT-2e updates 4 times per day. Actual-generation power forecasts on the Jua platform refresh every 15 minutes.

3. Developer integration ease. Production pipelines need schema stability, large-payload support, and documented SDKs. Jua for Energy exposes a REST API with Apache Arrow support and a Python SDK via pip install jua. ENTSO-E grid data integrates natively. The query engine is documented at query.jua.ai/docs, and the developer dashboard at developer.jua.ai.

4. Hindcast and backtest availability. Quant funds and trading houses require years of historical forecast data to validate strategies before deploying capital. Hindcast data is available across multiple Jua and third-party models on the Jua platform. Athena runs a full backtest in approximately 5 minutes via natural-language query.

Strategic Trade-offs in Choosing a Forecast API

Free NWP aggregators like Open-Meteo reduce procurement cost to zero but provide no ensembles, no hindcasts, no benchmarking surface, and no productised refresh schedule. Point-solution vendors like Solcast specialise in one variable, such as solar irradiance, and require a separate contract, schema, and pipeline for wind. AI weather research outputs such as Aurora and GraphCast deliver raw model files without workflow tooling, which forces quant teams to build ingestion pipelines, ensemble logic, and benchmarking harnesses themselves. That work consumes engineering capacity that should go into alpha research.

Manual pipelines assembled from ECMWF grib files, spreadsheets, and consultancy reports produce a coherent view of the day only after the market has already moved. The strategic case for a single production-grade workspace is that it removes the integration tax across all four dimensions at once.

Head-to-Head Comparison of Leading Providers

The following table benchmarks five providers across the four dimensions that determine production readiness, which are accuracy, refresh cadence, integration tooling, and hindcast availability, so you can see where each solution delivers and where it falls short for European renewable trading.

Implementation Example with the Jua Python SDK

The Jua Python SDK installs from PyPI and provides forecast access, hindcast queries, and weather-parameter standardisation across all 25+ models on the platform.

pip install jua from jua import JuaClient client = JuaClient(api_key="YOUR_API_KEY") # Request EPT-2 100m wind forecast for northern Germany forecast = client.forecast( model="ept-2", variables=["wind_speed_100m", "surface_solar_radiation_downwards"], latitude=53.5, longitude=10.0, horizon_hours=240, ) print(forecast.to_dataframe()) 

Benchmarking best practices start with validation against held-out ground-truth observations rather than model reanalysis, and StationBench provides an open-source harness against 10,000+ stations. You then evaluate CRPS alongside RMSE, because RMSE measures deterministic skill and CRPS measures probabilistic calibration, which is the relevant metric for position sizing under uncertainty. You also run hindcasts over at least two full years to capture seasonal variation in wind and solar regimes. Finally, you compare dissemination time as well as accuracy, because a forecast that arrives 2.5 hours earlier than the competing operational run at the same cycle provides a structural edge.

Test EPT-2 on your own region and variables and benchmark against 25+ models in under 5 minutes using the SDK you just saw.

Readiness Checklist for Production Use

Before selecting a Europe renewable forecast API for production use, confirm several concrete points. First, verify that the provider publishes peer-reviewed accuracy benchmarks against ECMWF HRES on 100 m wind and SSRD at station level, not just on reanalysis grids, because without station-level validation you cannot trust performance on real portfolios. Second, ensure the refresh cadence matches your intraday trading window, since four updates per day are insufficient for intraday positions and 24 updates per day represent the current ceiling for acting on sub-hour-old atmospheric state. Third, confirm that the API exposes a Python SDK with Apache Arrow support for continental, multi-variable, multi-model payloads, which removes the overhead of stitching together separate data feeds.

Fourth, check that hindcast data is available for at least two years across the models you plan to use in production so you can run robust backtests. Fifth, require native ENTSO-E grid integration so power forecasts are capacity-weighted against actual installed capacity rather than derived from raw weather outputs alone. Sixth, ensure an ensemble variant with published CRPS benchmarks exists so you can size positions using probabilistic information instead of relying only on point forecasts.

Common Pitfalls in Renewable Forecast API Selection

The two most common failure modes in Europe renewable forecast API selection are accepting vendor accuracy claims without independent validation and deploying a strategy without hindcast-based backtesting. Several AI weather model providers publish headline RMSE numbers evaluated on reanalysis grids such as ERA5 rather than against real ground-station observations. Reanalysis-evaluated RMSE is systematically lower than station-evaluated RMSE because reanalysis is itself a model output, not a measurement. EPT-2’s benchmarks in arXiv:2507.09703 are run against more than 10,000 real ground stations via StationBench, with no post-processing or station fine-tuning, which is the standard that any provider making accuracy claims should meet.

A second pitfall appears when teams backtest strategies on reanalysis weather data instead of on actual historical forecast outputs. A strategy backtested on reanalysis will overestimate performance because reanalysis has no forecast error at lead time zero. Hindcasts, which are retrospective forecasts generated with the same model and initialisation procedure as operational runs, provide the correct input for strategy validation. Most free and point-solution APIs do not provide them.

Frequently Asked Questions

How does EPT-2 accuracy on 100 m wind compare to ECMWF HRES, and how is that benchmark conducted?

EPT-2 outperforms ECMWF HRES on 100 m wind speed across the full 0–240 hour lead-time range, measured by RMSE. The benchmark uses the StationBench methodology described earlier, which scores against real ground-station observations rather than reanalysis grids and is stricter because it tests the model against actual measurements instead of another model’s output. EPT-2e, the ensemble variant, also beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time, which means it delivers superior probabilistic skill with 10 members against the ENS 50. Both results appear in the peer-reviewed technical report arXiv:2507.09703.

What refresh cadence does Jua for Energy provide, and why does it matter for intraday trading?

Jua’s rapid-refresh model delivers the 24 times daily cadence mentioned earlier, with the ensemble variant updating 4 times per day and actual-generation forecasts refreshing every 15 minutes. Traditional NWP infrastructure, including ECMWF HRES, runs its full algorithm twice a day, with smaller supplementary runs bringing the industry total to roughly four global forecasts per 24 hours. Between those runs, traders work with stale numbers. Intraday power markets in Germany, Great Britain, and France clear on 15-minute and 30-minute intervals, so a forecast that is six hours old at the point of trade is not fit for intraday positioning. The 24 times daily cadence of EPT2-RR means the forecast a trader acts on reflects atmospheric state from less than one hour ago rather than six.

How does hindcast availability on the Jua platform support quantitative backtesting?

Hindcast data is available across multiple Jua and third-party models on the Jua platform. A hindcast is a retrospective forecast generated with the same model and initialisation procedure as operational runs, which preserves the forecast error structure that a live trading strategy would have experienced, unlike reanalysis data that has no forecast error at lead time zero. Quant developers install the Python SDK via pip install jua, query hindcast data programmatically through the REST API, and run backtests either directly through the SDK or via Athena in natural language. A typical Athena backtest resolves in approximately 5 minutes. ERA5 reanalysis data is also available from 1990 onward as a long-horizon reference, and the combination of model hindcasts and ERA5 ground truth gives quant teams the inputs required to validate a systematic strategy before deploying capital.

Does Jua for Energy replace an existing ECMWF subscription?

Jua for Energy runs alongside an existing ECMWF subscription rather than replacing it. ECMWF HRES and ENS are available on the Jua platform as native integrations under the same unified schema as EPT-2 and EPT-2e, and ECMWF AIFS, ECMWF’s own AI model, also runs on the platform. Jua for Energy displaces the plumbing around the ECMWF feed, including the in-house grib pipeline, the manual benchmarking, the morning-briefing analyst, and the dashboard stitching. The 7–9 a.m. manual prep routine compresses into a single workspace, refreshed up to 24 times a day, where every model appears on the same screen with one schema and one API. Customers operating multi-GW portfolios keep their ECMWF subscription and add Jua for Energy as the workspace that makes all of it actionable before the market opens.

Next Steps for Evaluating EPT-2

The four dimensions that determine production-grade API selection, which are accuracy, refresh frequency, integration ease, and hindcast availability, converge in a single workspace when the underlying model is EPT-2 and the agent is Athena. Jua for Energy already serves major European utilities, commodity trading houses, and quant funds across four continents, with sales cycles that compress to weeks once the live benchmark runs. The benchmark acts as the deal trigger, because you pick your region, pick your variable, and let the numbers speak.

Schedule your EPT-2 benchmark and run it head-to-head against your current provider on your own data in under 5 minutes.