{"id":603,"date":"2026-06-17T05:00:26","date_gmt":"2026-06-17T05:00:26","guid":{"rendered":"https:\/\/jua.ai\/articles\/ai-weather-forecasting-renewables-europe\/"},"modified":"2026-06-17T05:00:26","modified_gmt":"2026-06-17T05:00:26","slug":"ai-weather-forecasting-renewables-europe","status":"publish","type":"post","link":"https:\/\/jua.ai\/articles\/ai-weather-forecasting-renewables-europe\/","title":{"rendered":"AI Weather Forecasting for Renewables: EPT-2 Beats ECMWF"},"content":{"rendered":"<p><em>Written by: Olivier Lam, Physical AI Team, Jua.ai AG<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for European Renewables Traders<\/h2>\n<ul>\n<li>EPT-2 outperforms ECMWF HRES on 100 m wind and surface solar radiation at every lead time from 0\u2013240 hours according to June 2026 benchmarks.<\/li>\n<li>Jua\u2019s rapid-refresh variant (EPT-2 RR) updates up to 24 times per day, far exceeding the 2\u20134 daily cycles of traditional NWP models.<\/li>\n<li>Sub-daily probabilistic forecasts from EPT-2e deliver measurable P&amp;L gains, with accuracy improvements translating to \u20ac1.5\u20133 million annual savings per GW for wind and solar portfolios.<\/li>\n<li>The Jua platform combines EPT-2, ECMWF, and other models behind a unified API and Athena AI agent that generates 90-second natural-language briefings.<\/li>\n<li><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\">See Athena and EPT-2 on your own assets in a live workflow demo<\/a> and connect AI weather intelligence directly to your trading stack.<\/li>\n<\/ul>\n<h2>EPT-2 as the AI Successor to the European Weather Model<\/h2>\n<p>ECMWF HRES has been the forty-year gold standard in deterministic NWP, running at 9 km resolution on two to four daily cycles. ECMWF&#8217;s AI Forecasting System (AIFS) is a machine-learning model trained on ECMWF reanalysis and operational data, with faster inference but the same dissemination schedule. EPT-2 belongs to a different category.<\/p>\n<p>EPT, the Earth Physics Transformer, is a general spatiotemporal transformer foundation model that learns the governing physics of complex systems directly from observational data. It is not a narrow weather model; it is a physics foundation model fine-tuned for atmospheric prediction. The architecture learns conservation laws of mass, momentum, and energy at the representation level, so outputs stay physically consistent by construction instead of being post-processed into plausibility. That physical grounding allows EPT-2 to generalize across more than 5 petabytes of weather and climate data from 120+ distinct sources and to hold accuracy when benchmarked against more than 10,000 real ground stations on open-source StationBench, with no post-processing or station fine-tuning.<\/p>\n<p>The operational difference that traders feel is update frequency. ECMWF HRES runs two to four times per day. EPT-2 RR, Jua&#8217;s rapid-refresh variant, updates up to 24 times per day. EPT-2 HRRR delivers the same high-cadence updates at up to ~5 km native resolution over Europe. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">EPT-2 delivers hourly global weather updates at 6\u00d7 higher temporal and spatial resolution than comparable AI models, outperforming leading AI weather models and traditional numerical baselines across all forecast horizons on RMSE.<\/a><\/p>\n<p><a href=\"https:\/\/athena.jua.ai\" target=\"_blank\"><strong>Compare EPT-2 against ECMWF HRES on your portfolio&#8217;s exact coordinates at athena.jua.ai.<\/strong><\/a><\/p>\n<h2>AI Forecast Improvements for Wind and Solar in Europe<\/h2>\n<p>Wind and solar generation drive most intraday price volatility in European power markets. A wind ramp not predicted, or a solar dip not flagged, turns directly into imbalance costs and missed trading windows. Traditional NWP&#8217;s four-runs-per-day cadence leaves traders working from stale numbers for hours between cycles.<\/p>\n<p>Sub-daily probabilistic forecasts address staleness and uncertainty at the same time. Higher update frequency closes the gap between reality and the last forecast. Ensemble outputs quantify the spread of possible outcomes instead of returning a single deterministic trace, so traders can size positions around probability-weighted expected values and tail risks. A probabilistic solar forecast showing a 40% chance that a fast-moving storm front will push output below 70 MW for a given hour lets an IPP adjust its day-ahead bid from 80 MW to 65 MW before the market closes.<\/p>\n<p>That kind of probabilistic decision-making requires an ensemble forecast that is both accurate and operationally available. EPT-2e, the ensemble variant of EPT-2, beats the 50-member ECMWF ENS mean on both RMSE and CRPS (Continuous Ranked Probability Score, a measure of probabilistic forecast skill) at virtually every lead time, as documented in <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a>. EPT-2e updates four times per day. No AI peer has shipped a productised ensemble equivalent. <a href=\"https:\/\/meticulousresearch.com\/product\/renewable-energy-forecasting-software-market-6370\" target=\"_blank\" rel=\"noindex nofollow\">In 2026, Europe holds the largest share of the global renewable energy forecasting software market, driven by mandatory forecasting requirements for market participation and wind and solar penetration rates exceeding 40\u201360% in countries such as Denmark, Germany, and Spain.<\/a><\/p>\n<p><a href=\"https:\/\/athena.jua.ai\" target=\"_blank\"><strong>Test EPT-2e&#8217;s probabilistic forecasts on your wind and solar sites and inspect the ensemble spread and CRPS improvement at athena.jua.ai.<\/strong><\/a><\/p>\n<h2>Head-to-Head Comparison on European Renewables Variables<\/h2>\n<p>The table below compares EPT-2, ECMWF HRES, and ECMWF AIFS on the variables that drive European renewables trading P&amp;L. It focuses on three levers that turn forecasts into trading alpha: accuracy on renewables-critical parameters, update frequency, and operational availability. EPT-2&#8217;s edge comes from leading on all three dimensions at once. All EPT-2 benchmark figures are sourced from <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a>. ECMWF HRES and AIFS specifications are sourced from ECMWF public documentation.<\/p>\n<table>\n<thead>\n<tr>\n<th>Model<\/th>\n<th>100 m Wind RMSE vs. HRES (0\u2013240 h)<\/th>\n<th>Surface Solar Radiation (SSRD)<\/th>\n<th>Update Frequency<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>EPT-2<\/strong> (<a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a>)<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">Beats ECMWF HRES at every lead time 0\u2013240 h<\/a><\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">Beats ECMWF HRES at every lead time 0\u2013240 h<\/a><\/td>\n<td><a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Up to 24\u00d7\/day (EPT-2 RR); EPT-2e 4\u00d7\/day<\/a><\/td>\n<\/tr>\n<tr>\n<td><strong>ECMWF HRES<\/strong><\/td>\n<td>Benchmark, 40 years of NWP leadership<\/td>\n<td>Benchmark, industry reference<\/td>\n<td>2\u20134\u00d7\/day<\/td>\n<\/tr>\n<tr>\n<td><strong>ECMWF AIFS<\/strong><\/td>\n<td>Competitive with HRES, no published head-to-head vs. EPT-2 at all lead times<\/td>\n<td>Available, no published head-to-head vs. EPT-2 at all lead times<\/td>\n<td>Aligned with ECMWF dissemination schedule<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>ECMWF HRES remains the universal benchmark and a respected signal. Jua for Energy runs it alongside EPT-2 and ECMWF AIFS under a unified schema and a single API rather than replacing it. EPT-2 sets a new accuracy ceiling above HRES on 100 m wind and SSRD, the two variables that most directly determine renewables generation and intraday price moves.<\/p>\n<p><a href=\"https:\/\/athena.jua.ai\" target=\"_blank\"><strong>Validate these benchmark results on your own renewables portfolio by running EPT-2 head-to-head against your current provider at athena.jua.ai.<\/strong><\/a><\/p>\n<h2>Why Sub-Daily Probabilistic Forecasts Move Renewables P&amp;L<\/h2>\n<p>Forecast accuracy improvements convert directly into portfolio economics. A 1 GW wind portfolio that gains four percentage points of forecast accuracy saves approximately \u20ac1.5 million per year under typical European hedging and imbalance-penalty structures. A 1 GW solar portfolio at the same accuracy gain saves approximately \u20ac3 million per year. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Jua&#8217;s forecasts carry an estimated $1.5 million P&amp;L impact per gigawatt annually in European energy markets, scaling to hundreds of millions for large portfolios.<\/a> Operators running multi-GW portfolios scale these economics linearly.<\/p>\n<p>Those economics now sit under greater pressure. <a href=\"https:\/\/energy.ec.europa.eu\/news\/eu-electricity-trading-day-ahead-markets-becomes-more-dynamic-2025-10-01_en\" target=\"_blank\" rel=\"noindex nofollow\">Europe&#8217;s day-ahead electricity market transitioned from hourly to 15-minute trading intervals on 30 September 2025 for delivery on 1 October 2025 under SDAC<\/a>, compressing the decision window and raising the cost of stale forecasts. <a href=\"https:\/\/meticulousresearch.com\/product\/renewable-energy-forecasting-software-market-6370\" target=\"_blank\" rel=\"noindex nofollow\">AI-powered forecasting systems achieve accuracy improvements of 20\u201330% compared to traditional NWP methods, particularly for short-term horizons and ramp event prediction.<\/a><\/p>\n<p>Jua for Energy&#8217;s power forecast surface refreshes actual generation data every 15 minutes across Germany, Great Britain, France, the Netherlands, and Belgium, with a Fundamental Model running out to 20 days. EPT-2 RR&#8217;s 24-runs-per-day cadence gives traders the next forecast hours before the next traditional NWP cycle lands. Between those updates, divergence alerts fire the moment two models disagree on a key variable, and correction alerts fire the moment a model revises its own output, surfacing trade windows as they open rather than after the market has already repriced.<\/p>\n<h2>How Jua for Energy Fits Meteorology, Quant, and Trading Workflows<\/h2>\n<p>Jua for Energy runs alongside existing ECMWF subscriptions instead of replacing them. The integration path mirrors how meteorologists, quant developers, and traders work together when they adopt a new forecasting signal.<\/p>\n<p>Meteorologists start by validating the model. The Jua platform exposes more than 25 models, including 10 proprietary AI models from the EPT family and 15 third-party NWP and AI models such as ECMWF HRES, ENS, AIFS, NOAA GFS, DWD ICON, Microsoft Aurora, and GraphCast, through a single benchmarking surface. Any region and any variable can be compared head-to-head in under 30 seconds. EPT-2 and EPT-2e are documented in peer-reviewed technical reports on arXiv (<a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">2507.09703<\/a> and <a href=\"https:\/\/arxiv.org\/abs\/2410.15076\" target=\"_blank\" rel=\"noindex nofollow\">2410.15076<\/a>), which meets the evidentiary standard internal meteorology teams require before they champion a new model.<\/p>\n<p>Once meteorologists sign off, quant developers integrate the signal. Running <code>pip install jua<\/code> installs the Python SDK. The REST API exposes all models through a unified schema with Apache Arrow support for large payloads. Hindcast data across multiple Jua and third-party models supports backtesting and strategy development. Work that often takes a quant team a quarter to build on other stacks stands up in days.<\/p>\n<p>Traders then consume the validated and integrated forecasts. Athena, Jua&#8217;s AI agent instrumented with the Jua for Energy tool surface, turns a natural-language question into a briefing, a benchmark, a backtest, or a custom widget in about 90 seconds. Day-Ahead and Intraday briefings auto-refresh on every new model run and cover model consensus, model deltas, convergence tracking, and price implications. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Athena turns raw physics predictions from EPT-2 into actionable context by reading market conditions and modeling participant behavior.<\/a> The 7\u20139 a.m. manual prep routine of downloading grib files, waiting for the meteorologist&#8217;s briefing, and stitching together terminal screens compresses into a single workspace that surfaces forecasts at the resolution traders actually need. EPT-2 HRRR delivers up to ~5 km native resolution over Europe, and the Jua platform product surface supports up to 1 km resolution for European coverage.<\/p>\n<p>Customers including Axpo, TotalEnergies, Statkraft, EnBW, EDF, and Hydro-Qu\u00e9bec already execute daily trading decisions on the platform across four continents. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Sales cycles often shorten<\/a> for trading houses that run a live benchmark against their current provider, because the deal trigger is the performance data rather than the slide deck.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>Which AI model leads European wind and solar forecasting in 2026?<\/h3>\n<p>EPT-2, the flagship model in Jua&#8217;s Earth Physics Transformer family, leads on the two variables that most directly drive European renewables P&amp;L: 100 m wind speed and surface solar radiation. It maintains a clear accuracy advantage over ECMWF HRES across the full 10-day forecast horizon, as shown in the June 2026 technical report on <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a>, benchmarked against more than 10,000 real ground stations on StationBench with no post-processing. EPT-2e, the ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time. Microsoft Aurora does not provide surface solar radiation output, so EPT-2 is the only AI model with a complete renewables variable set at this accuracy level. ECMWF HRES remains the universal benchmark and continues to run alongside EPT-2 on the Jua platform.<\/p>\n<h3>How often do AI weather models update compared with ECMWF?<\/h3>\n<p>ECMWF HRES runs two to four times per day. Most AI weather peers, including Microsoft Aurora, Google DeepMind GraphCast, and ECMWF AIFS, follow a similar four-runs-per-day research cadence without a productised operational refresh schedule. EPT-2 RR, Jua&#8217;s rapid-refresh variant, updates up to 24 times per day, which gives a 6\u201312\u00d7 update frequency advantage. EPT-2e updates four times per day. Actual-generation power forecasts on the Jua for Energy platform refresh every 15 minutes across five European countries. Traders using Jua for Energy see the next forecast hours before the next traditional NWP cycle lands and receive divergence and correction alerts as soon as a model revises its output, instead of discovering the change after the market has already moved.<\/p>\n<h3>What accuracy gains translate into trading P&amp;L for renewables portfolios?<\/h3>\n<p>The market-sizing economics rely on a standardised methodology that applies across European hedging and imbalance-penalty structures. A four-percentage-point accuracy improvement serves as the baseline and scales linearly with portfolio size. These savings align with the RMSE improvement that EPT-2 delivers over ECMWF HRES on 100 m wind and surface solar radiation across the full 0\u2013240 hour range, consistent with the published benchmark results. Customer-specific P&amp;L outcomes depend on portfolio size, market structure, and hedging strategy. Jua for Energy provides forecasts and analysis, while trading and dispatch decisions remain with the customer.<\/p>\n<h2>Conclusion: EPT-2 Sets the Benchmark, Athena Delivers the Briefing<\/h2>\n<p>The June 2026 EPT-2 benchmarks show a clear result on European renewables variables. On 100 m wind and surface solar radiation, EPT-2 establishes an accuracy lead over ECMWF HRES across all lead times. EPT-2e improves probabilistic skill over the 50-member ECMWF ENS mean on RMSE and CRPS. EPT-2 RR runs up to 24 times per day, which is 6\u201312\u00d7 the cadence of traditional NWP. Inference costs range from about $0.20 to $15 per simulation on a single GPU, compared with roughly \u20ac1,000\u2013\u20ac20,000 for a traditional NWP run on HPC, delivering around four orders of magnitude lower cost without sacrificing forecast quality.<\/p>\n<p>Jua is a foundation model and agent company. EPT is a general physics foundation model, and Athena is an AI agent. Jua for Energy is the first applied product built on both. The architecture learns physics, and the domain becomes a variable. For European energy traders, meteorologists, and quant teams, the practical outcome is a single workspace where ECMWF, AIFS, Aurora, GraphCast, and EPT-2 run on the same screen under one schema, refreshed on the cadence of the underlying physics, with an analyst layer that answers questions in about 90 seconds. Teams move before the market does.<\/p>\n<p><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>See EPT-2 head-to-head against your current forecast provider in a live trading scenario. Book a demo.<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Jua&#8217;s EPT-2 outperforms ECMWF HRES at every lead time, saving wind &amp; solar traders \u20ac1.5\u20133M per GW annually. See Europe&#8217;s most accurate AI forecast.<\/p>\n","protected":false},"author":103,"featured_media":602,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[11],"tags":[],"class_list":["post-603","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-weather-forecasting"],"_links":{"self":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts\/603","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/users\/103"}],"replies":[{"embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/comments?post=603"}],"version-history":[{"count":0,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts\/603\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media\/602"}],"wp:attachment":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media?parent=603"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/categories?post=603"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/tags?post=603"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}