Written by: Olivier Lam, Physical AI Team, Jua.ai AG | Last updated: June 30, 2026
Key Takeaways for Solar Trading Teams
- EPT-2 currently leads 2026 solar forecast rankings on deterministic SSRD accuracy across the full 0–240 hour horizon.
- The rapid-refresh variant updates up to 24 times per day, which lets traders react to solar revisions before markets move.
- EPT-2e, the ensemble version, beats the 50-member ECMWF ENS mean on RMSE and CRPS across almost all horizons, so probabilistic hedging becomes more precise.
- Accuracy gains have direct P&L impact: a 1 GW solar portfolio can save about €3 M per year from a four-point SSRD improvement.
- Run a live Jua benchmark and see your own region’s solar forecast performance in under five minutes.
Forecast Horizons Mapped to Trading Use Cases
The table below links each forecast approach to the metrics that shape real trading utility. All EPT-2 accuracy claims reference the EPT-2 technical report (arXiv:2507.09703).
| Approach | Deterministic SSRD Accuracy vs. HRES | Ensemble Available | Update Frequency |
|---|---|---|---|
| Foundation model (EPT-2, Jua for Energy) | Higher SSRD skill than ECMWF HRES across 0–240 h | Yes, EPT-2e, which beats ECMWF ENS mean on RMSE and CRPS | Up to 24×/day (EPT-2 RR), 4×/day (EPT-2e) |
| NWP incumbent (ECMWF HRES) | The long-running deterministic benchmark | Yes, 50-member ENS | 2–4×/day |
| AI peer (Aurora, GraphCast) | Aurora has no SSRD output, GraphCast SSRD remains unverified versus HRES across horizons | No productised ensemble | Typically 4×/day |
| Satellite-derived (Solcast, Solargis) | High skill at very short horizons (<6 h), weaker beyond day-ahead | Limited probabilistic products | Varies by provider |
Compare Jua with your current feed and view EPT-2 head-to-head against your existing solar forecast provider.
Most Accurate Solar Forecast Models in 2026
This ranking reflects deterministic SSRD accuracy across 0–240 hours, update cadence, and production readiness as of June 2026.
1. Jua for Energy (EPT-2). EPT-2 shows higher surface solar radiation skill than ECMWF HRES at all tested lead times, based on more than 10,000 ground stations on open-source StationBench with no post-processing or station tuning. That deterministic edge pairs with EPT-2 RR’s up-to-24-times-daily refresh, so traders can adjust positions before the market fully re-prices new solar expectations. For probabilistic workflows, EPT-2e beats the 50-member ECMWF ENS mean on RMSE and CRPS at almost every lead time, which sharpens hedging and imbalance-cost management. Native resolution reaches 5 km over Europe (EPT-2 HRRR), and the Jua for Energy surface supports up to 1 km for site-level dispatch decisions. Jua builds foundation models and agents, and Jua for Energy is the first applied product on that stack.
2. ECMWF HRES. This model remains the 40-year deterministic benchmark. It runs at 9 km resolution and updates 2–4 times per day. HRES does not provide a native SSRD ensemble at the deterministic tier, yet it still serves as the universal reference for regulated utilities and trading houses.
3. ECMWF ENS. This 50-member ensemble is the long-standing probabilistic reference. It supplies spread around the HRES deterministic line, and its update cadence matches HRES. EPT-2e now exceeds the ENS mean on RMSE and CRPS at nearly all lead times, which gives traders a more accurate distribution to trade against.
4. ECMWF AIFS. AIFS is ECMWF’s AI forecasting system. It is available on the Jua for Energy platform alongside EPT-2 and HRES under a unified schema. Many desks use it as a third opinion in multi-model consensus workflows.
5. NOAA GFS. GFS offers a free deterministic baseline. It adds value as a diversity signal in ensemble blends and as a cost-free cross-check against paid feeds.
6. Solcast. Solcast focuses on satellite-derived irradiance with strong near-term skill. It fits nowcasting and very short intraday horizons best. Day-ahead and multi-day deterministic performance trails NWP-anchored models.
7. Solargis. Solargis combines long-record satellite data with NWP and has a strong track record in resource assessment. For operational forecasting, its cadence and day-ahead SSRD accuracy sit behind EPT-2 and ECMWF HRES.
ECMWF vs GFS for Solar Trading Decisions
ECMWF HRES generally beats NOAA GFS on surface solar radiation at day-ahead and multi-day horizons. ECMWF’s data assimilation, higher native resolution of 9 km versus roughly 13 km for GFS, and stronger cloud physics reduce SSRD RMSE across Europe and most mid-latitude regions. GFS still matters operationally as a free, open baseline and as a diversity signal in multi-model blends, while ECMWF’s two-week outlook remains the main reference for traders repricing renewable risk and system tightness, a role GFS does not match. For solar-specific single-model decisions, ECMWF is usually stronger. For cost-sensitive setups or redundancy, GFS adds a useful second view. Both models run natively on the Jua for Energy platform under one schema.
Update Cadence and Its Impact on Intraday P&L
Update frequency directly shapes intraday P&L exposure. Traditional NWP systems face supercomputer cost limits, and a single NWP simulation consumes about 8,400 kWh and costs €1,000–€20,000, which caps ECMWF and GFS at 2–4 global runs per day. AI peers such as Microsoft Aurora and Google DeepMind’s GraphCast typically run four times daily in research-grade setups, without a published operational schedule. EPT-2e updates four times per day. EPT-2 RR, the rapid-refresh deterministic variant, updates up to 24 times per day, and actual-generation power forecasts on Jua for Energy refresh every 15 minutes. A single EPT-2 inference runs on one GPU in minutes at roughly 0.25 kWh and $0.20–$15, which is about four orders of magnitude cheaper than an equivalent NWP run. That cost profile is what makes 24× daily refresh practical in live trading.
Best Solar Irradiance Forecast for Day-Ahead Trading in 2026
This table focuses on day-ahead workflows and combines accuracy, cadence, and access method, so trading teams can see how each model fits into real infrastructure.
| Model / Platform | SSRD Accuracy vs. HRES (0–240 h) | Update Frequency | Access Method |
|---|---|---|---|
| Jua for Energy (EPT-2) | Higher SSRD skill than HRES at all tested lead times | Up to 24×/day (EPT-2 RR), 4×/day (EPT-2e) | REST API, Python SDK (pip install jua), Athena agent, platform UI |
| ECMWF HRES | Long-running benchmark | 2–4×/day | MARS (member access), also on Jua platform |
| ECMWF ENS | Established probabilistic reference | 2–4×/day | MARS, also on Jua platform |
| ECMWF AIFS | Competitive AI variant, no full SSRD comparison versus HRES yet | 4×/day | Available on Jua platform |
| Microsoft Aurora | No published SSRD output | Typically 4×/day | Research API, available on Jua platform |
| NOAA GFS | Free deterministic baseline | 4×/day | NOMADS (open), available on Jua platform |
| Solcast | Strong <6 h, weaker at day-ahead and beyond | Varies | Solcast API |
SDK Integration and Hindcast Backtesting for Quants
pip install jua installs the Python SDK from PyPI. The REST API exposes more than 25 models, including 10 proprietary EPT-family variants and 15 third-party NWP and AI models, through a single schema with Apache Arrow support for large payloads. Hindcast data spans multiple Jua and third-party models, so quant teams can run multi-year backtests against historical SSRD forecasts. Documentation lives at docs.jua.ai, and the developer dashboard at developer.jua.ai. Work that might take a quarter to build in-house often stands up in days on this stack.
Start your integration and pipe EPT-2 SSRD forecasts into your own models for a first backtest.
5-Minute Live Benchmarking for Utility Meteorologists
The Jua for Energy benchmarking surface puts more than 25 models on one platform. A meteorologist selects a region, a variable such as SSRD, and a time window, then runs a head-to-head comparison that returns in seconds. Athena, Jua’s AI agent instrumented with the Jua for Energy tool surface, can run the same benchmark from a natural-language prompt and deliver a written comparison in about 90 seconds. Jua already serves major utilities across four continents, including several of Europe’s largest energy companies. Those teams often cite the first live benchmark as the moment they switch from sceptical to convinced, because they see their own regions and assets in the numbers.
Market Economics of Forecast Accuracy
A 1 GW solar portfolio that gains four percentage points of SSRD forecast accuracy saves about €3 M per year under typical hedging and imbalance-penalty structures. A 1 GW wind portfolio that gains the same improvement saves about €1.5 M per year, and multi-GW operators scale these effects roughly linearly. European energy traders already deploy AI tools to anticipate model revisions before markets re-price. In that context, a 24× daily update cadence becomes a structural trading edge rather than a convenience feature.
Conclusion: How EPT-2 Changes Solar Trading Workflows
As of June 2026, solar irradiance forecasting for energy trading has a clear front-runner. EPT-2’s documented accuracy lead over HRES, combined with up-to-24-times-daily refresh through EPT-2 RR and single-SDK access to more than 25 models, makes it a strong single-model choice for day-ahead and multi-day positioning. Jua builds foundation models and agents, and Jua for Energy is the first applied product on that architecture. The same EPT design that now leads atmospheric prediction is domain-agnostic, with the atmosphere as the first physical system and energy trading as the first market. You can run a region-specific SSRD benchmark on the Jua platform in under five minutes and see exactly how your current provider compares.
See your region’s results and run a live solar benchmark on the Jua platform today.
Frequently Asked Questions
What makes a solar forecast model suitable for energy trading?
Three criteria define fitness for trading workflows. First, deterministic SSRD accuracy across the horizon that matches the trade, since day-ahead markets need skill out to 36–48 hours and multi-day gas and power positioning needs skill out to 240 hours. Second, update frequency, because a model that refreshes 24 times per day lets traders act on revised solar output before markets move, while a model that refreshes twice daily can leave desks exposed to silent revisions. Third, ensemble availability, because probabilistic forecasts quantify the spread around the deterministic line and support hedging and imbalance-cost control. EPT-2 leads on all three criteria as of June 2026, with higher SSRD skill than ECMWF HRES, rapid-refresh EPT-2 RR, and EPT-2e ensemble performance that exceeds the 50-member ECMWF ENS mean on RMSE and CRPS at almost every lead time.
How does Jua for Energy connect to existing trading infrastructure?
Integration follows two main paths. Quant developers and systematic funds install the Python SDK with pip install jua. The REST API then exposes more than 25 models through a single schema with Apache Arrow support for large payloads, and hindcast data supports multi-year backtesting. Utilities and trading houses that already run dispatch, risk, and trading tools connect Jua for Energy through the same REST API, with a direct ENTSO-E integration for European grid data. All models on the platform, including ECMWF HRES, ENS, AIFS, NOAA GFS, DWD ICON, Aurora, and GraphCast, share a unified schema, so teams can add or swap models without rebuilding pipelines. Documentation is available at docs.jua.ai.
Does Jua for Energy replace an ECMWF subscription?
Jua for Energy complements ECMWF rather than replacing it. Most serious customers keep their ECMWF subscription and use Jua for Energy to replace the surrounding plumbing, including in-house GRIB pipelines, manual benchmarking, morning briefings, and dashboard stitching. ECMWF AIFS, ECMWF’s own AI model, runs natively on the Jua for Energy platform. In practice, users work in a single workspace where ECMWF HRES, ENS, AIFS, EPT-2, and many other models appear on the same screen, under one schema, refreshed on the same cycle as the underlying physics.
What is Athena and how does it support solar workflows?
Athena is Jua’s AI agent, instrumented with the Jua for Energy tool surface. It accepts natural-language objectives and turns them into deliverables such as benchmarks, briefings, backtests, or custom widgets, usually in about 90 seconds for typical queries and about five minutes for backtests. In solar workflows, a meteorologist can ask Athena to compare EPT-2 SSRD with ECMWF HRES over a region and time window and receive a written comparison with the underlying data already rendered. Traders use Athena to generate intraday briefings that update on every new model run and replace the manual 7–9 a.m. preparation routine. Athena is an AI agent, and the atmosphere plus energy trading form the first physical system and market it supports.
How are EPT-2’s accuracy claims validated?
EPT-2 is benchmarked against more than 10,000 ground stations using open-source StationBench with no post-processing or station fine-tuning. Results appear in a peer-reviewed technical report at arXiv:2507.09703. The evaluation covers 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation across the full 0–240 hour lead-time range. EPT-2e ensemble results, which exceed the 50-member ECMWF ENS mean on RMSE and CRPS at almost every lead time, are documented in the same report. The methodology is open, the station network is independent, and the numbers are reproducible. Customers who run live benchmarks on the Jua for Energy platform against their own regions and variables typically reach the same conclusions within minutes.