{"id":556,"date":"2026-06-11T05:01:49","date_gmt":"2026-06-11T05:01:49","guid":{"rendered":"https:\/\/jua.ai\/articles\/energy-market-analytics-europe\/"},"modified":"2026-06-11T05:01:49","modified_gmt":"2026-06-11T05:01:49","slug":"energy-market-analytics-europe","status":"publish","type":"post","link":"https:\/\/jua.ai\/articles\/energy-market-analytics-europe\/","title":{"rendered":"Energy Market Analytics Europe: AI-Powered Forecasting"},"content":{"rendered":"<p><em>Written by: Olivier Lam, Physical AI Team, Jua.ai AG<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>European energy markets are increasingly weather-driven, with renewables at 47.5% of consumption and shrinking room for forecast error.<\/li>\n<li>Legacy analytics stacks suffer from stale forecasts, silent model revisions, and meteorology teams that cannot scale across regions or trading cycles.<\/li>\n<li>Jua for Energy delivers up to 24 daily refreshes via EPT-2, live benchmarking across 25+ models, and Athena, an AI agent that turns natural-language queries into briefings and backtests in about 90 seconds.<\/li>\n<li>EPT-2 outperforms ECMWF HRES on every lead time for wind, temperature, and solar radiation, while EPT-2e beats the 50-member ECMWF ENS mean on RMSE and CRPS.<\/li>\n<li>Traders can <a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\">book a demo with Jua<\/a> to see live 25-model benchmarking on their region in under 5 minutes.<\/li>\n<\/ul>\n<h2>The Problem: Fragmented European Energy Market Analytics<\/h2>\n<p>European power markets are structurally weather-driven. <a href=\"https:\/\/www.ec.europa.eu\/eurostat\/web\/products-eurostat-news\/w\/ddn-20260114-1\" target=\"_blank\" rel=\"noindex nofollow\">Renewable energy sources accounted for 47.5% of gross electricity consumption in the EU in 2024<\/a>, supported by <a href=\"https:\/\/www.pv-magazine.com\/2024\/12\/18\/eu-solar-installations-hit-65-5-gw-in-2024-says-solarpower-europe\/\" target=\"_blank\" rel=\"noindex nofollow\">EU solar installations that grew about 4% year over year in 2024 while total solar capacity grew about 24%<\/a>. EU wind capacity grew about 6% over the same period. <a href=\"https:\/\/about.bnef.com\/insights\/clean-energy\/bloombergnefs-new-energy-outlook-2026-transition-to-newer-technologies-expanded-electrification-to-strengthen-nations-energy-security\" target=\"_blank\" rel=\"noindex nofollow\">BloombergNEF&#8217;s New Energy Outlook 2026 projects substantial growth in battery storage by 2035<\/a>, which compresses intraday balancing windows further. As variable generation rises and storage absorbs more of the residual load, the margin for forecast error shrinks.<\/p>\n<p>The analytics stack most European traders run has not kept pace. A typical workflow starts at 6 a.m., with overnight ECMWF and GFS runs downloaded as raw grib files, processed through an in-house pipeline, and cross-referenced with an internal meteorology team or a consultancy. Traders then stitch together spreadsheets, terminal screens, and vendor dashboards. <a href=\"https:\/\/www.bloomberg.com\/news\/articles\/2026-03-26\/energy-traders-turn-to-ai-to-forecast-the-weather-forecast?embedded-checkout=true\" target=\"_blank\">In Europe&#8217;s weather-driven energy markets, traders are increasingly turning to AI and machine-learning tools designed to forecast the forecast itself<\/a>, especially shifts in the ECMWF two-week outlook that reprice risk around heating demand, renewable output, and system tightness. The legacy stack cannot deliver that capability systematically, at scale, and in real time.<\/p>\n<p>Three specific failure modes drive the most revenue loss. Forecasts go stale between NWP runs. Model revisions occur silently and move the market before the desk notices. Internal meteorology teams cannot scale across more regions, assets, or trading cycles without adding headcount.<\/p>\n<p>Addressing these failure modes requires a different architecture. Traders need forecasts that refresh far more often than traditional NWP, a surface that benchmarks many models at once, and an analyst layer that scales without new hires. A foundation model paired with an agent delivers that architecture.<\/p>\n<p><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>See live 25-model benchmarking on your region in under 5 minutes.<\/strong><\/a><\/p>\n<h2>Jua\u2019s Physics Foundation Model and Agent for Energy Desks<\/h2>\n<p>Jua is a foundation model and agent company, and Jua for Energy is its first applied product. The underlying architecture combines the Earth Physics Transformer (EPT) family with the AI agent Athena. The core platform is domain-agnostic, while each physical system uses domain-specific data and fine-tuning. The relationship mirrors Anthropic and Claude Code, with a horizontal AI platform and a flagship vertical product.<\/p>\n<p>Applied to energy trading, the platform delivers three capabilities the legacy stack cannot match. Forecasts refresh up to 24 times per day. A live benchmarking surface spans more than 25 models and surfaces divergences and corrections the moment they occur. Athena, an AI agent, turns natural-language queries into briefings, benchmarks, backtests, and custom widgets in about 90 seconds. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Athena turns raw physics predictions from EPT-2 into trading intelligence by reading market context and modeling participant behavior<\/a>.<\/p>\n<h2>Stale Forecasts Between Runs vs 24x Daily Refreshes<\/h2>\n<p>A single traditional NWP simulation consumes about 8,400 kWh of compute and costs \u20ac1,000\u2013\u20ac20,000 to run on high-performance infrastructure. That cost ceiling limits the global NWP schedule to two to four full runs per day. Between runs, every trader on the desk sees the same stale numbers.<\/p>\n<p><a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">EPT-2 delivers hourly global weather updates<\/a>, and <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">outperforms ECMWF HRES on every lead time across the full 0\u2013240 hour range on 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation. EPT-2e beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time<\/a>. A single EPT-2 inference runs on a single GPU in minutes at about 0.25 kWh and $0.20\u2013$15. That is roughly four orders of magnitude cheaper than the equivalent NWP run, which makes 24x daily refreshes operationally viable.<\/p>\n<p>EPT-2 RR (rapid refresh) updates up to 24 times per day. EPT-2 HRRR delivers the same high-cadence output at up to 5 km native resolution over Europe. Actual-generation power forecasts on the Jua platform refresh every 15 minutes. In markets such as Germany, intraday cross-zonal gate closure occurs 30 minutes before physical delivery, so sub-15-minute analytics refresh becomes a functional requirement rather than a differentiator. Jua for Energy meets that requirement, while the legacy NWP stack does not.<\/p>\n<p>The accuracy underpinning those refreshes is documented in the peer-reviewed technical report <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a>. EPT-2 outperforms ECMWF HRES on every lead time across the full 0\u2013240 hour range on 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation. EPT-2e, the ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE (root mean square error) and CRPS (continuous ranked probability score) at virtually every lead time.<\/p>\n<h2>Silent Model Revisions vs Real-Time Alerts and Benchmarking<\/h2>\n<p>Traditional NWP models update silently, and mid-cycle revisions from ECMWF or GFS often reach prices before most desks have seen them. Divergence between two models, where one predicts a wind ramp and the other does not, creates a tradeable signal for whoever spots it first.<\/p>\n<p>Jua for Energy runs four alert types continuously across the 25+ model fleet. Divergence alerts fire the moment two or more models disagree on a key variable. Correction alerts fire the moment a model revises its own output between runs. Threshold alerts fire on user-defined conditions, such as 100 m wind exceeding a specified level in a defined zone. New model run alerts notify when a specific model&#8217;s latest forecast becomes available. All four alert types are filterable by zone and by PSR (Production Source Resource) type.<\/p>\n<p>The live benchmarking surface puts more than 25 models on a single platform. Traders see 10 proprietary AI models from the EPT family plus 15 third-party NWP and AI models, including ECMWF HRES, ECMWF ENS, ECMWF AIFS, NOAA GFS, GFS GraphCast, Microsoft Aurora, DWD ICON Global, and ICON-EU. Any region, any variable, any time window, and head-to-head comparison in seconds. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Jua serves major utilities across four continents, including some of Europe&#8217;s largest energy companies, as well as commodity traders and hedge funds, with sales cycles compressed to as little as two weeks<\/a>. The live benchmark typically triggers the deal.<\/p>\n<h2>Internal Meteorology Bottlenecks vs Athena Briefings<\/h2>\n<p>Internal meteorology teams produce daily morning briefings by hand, downscale ECMWF and GFS outputs into desk-specific views, and field ad-hoc forecast questions from the trading floor. The work is high-quality but slow and expensive. It cannot scale across more desks, regions, or asset classes without adding headcount. External consultancies fill gaps with delayed, generic reports that often arrive after the trade window has closed.<\/p>\n<p>Athena is Jua&#8217;s AI agent, instrumented with the Jua for Energy tool surface. A trader types a question in natural language, such as \u201cwhat is the 100 m wind forecast spread across models for northern Germany tonight?\u201d or \u201cbacktest a wind-ramp strategy on EPT-2e over the last two winters.\u201d Athena plans the steps, calls tools, evaluates intermediate outputs, and returns the answer, the underlying widget, or the full backtest report. Typical queries resolve in about 90 seconds, and backtests complete in about 5 minutes. Trading houses and quant desks describe Athena as \u201canother headcount, for free.\u201d<\/p>\n<p><a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Jua&#8217;s founders noted early in the company&#8217;s development: \u201cWe realized this is about human nature more than mother nature. Traders need a system that understands how the physical world moves markets.\u201d<\/a> Athena delivers that system as an analyst that works for the trader, not just a dashboard.<\/p>\n<p><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>Watch Athena resolve a live energy briefing query in about 90 seconds.<\/strong><\/a><\/p>\n<h2>Live Benchmark: EPT-2 vs ECMWF HRES in European Power Markets<\/h2>\n<p>Jua for Energy delivers live power forecasts for Germany (DE), Great Britain (GB), France (FR), the Netherlands (NL), and Belgium (BE). Coverage spans solar, wind onshore, wind offshore, total wind, total renewables, load, and residual load. Two models run on the same surface. A Fundamental Model combines the EPT weather forecast with installed-capacity data and runs out to 20 days. An Actual Generation Model refreshes every 15 minutes with a 48-hour horizon.<\/p>\n<p>The benchmark numbers behind those forecasts appear in <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a>. EPT-2 outperforms ECMWF HRES on every lead time from 0 to 240 hours on the four variables that drive European energy P&amp;L: 10 m wind, 100 m wind (critical for wind-turbine hub heights), 2 m temperature, and surface solar radiation (SSRD). As noted earlier, EPT-2e beats HRES and the ENS mean across all four energy-critical variables at every lead time from 0 to 240 hours. Microsoft Aurora has no SSRD output at all, so EPT-2 wins that variable by default. EPT-2 also beats Aurora on 10 m wind, 100 m wind, and 2 m temperature across the full 0\u2013240 hour range, and runs about 25% faster at inference.<\/p>\n<p><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<\/a>. For a multi-GW portfolio, those economics scale linearly. A 1 GW wind portfolio that gains four percentage points of forecast accuracy saves about \u20ac1.5 million per year. A 1 GW solar portfolio at the same accuracy gain saves about \u20ac3 million per year.<\/p>\n<h2>Side-by-Side View: Energy Market Analytics Platforms<\/h2>\n<table>\n<thead>\n<tr>\n<th>Capability<\/th>\n<th><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">Jua for Energy (EPT-2 + Athena)<\/a><\/th>\n<th>ECMWF HRES \/ ENS (NWP incumbent)<\/th>\n<th>Aurora \/ GraphCast (AI research outputs)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Deterministic accuracy vs. HRES (0\u2013240 h, 10 m wind, 100 m wind, 2 m temp, SSRD)<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2 beats HRES on every lead time and all four variables<\/a><\/td>\n<td>The 40-year benchmark itself<\/td>\n<td>Aurora loses to EPT-2 on 10 m wind, 100 m wind, 2 m temp across full range; no SSRD output<\/td>\n<\/tr>\n<tr>\n<td>Ensemble (probabilistic) forecasting<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2e beats 50-member ECMWF ENS mean on RMSE and CRPS at virtually every lead time<\/a><\/td>\n<td>ENS: 50-member gold standard for probabilistic NWP<\/td>\n<td>No productised ensemble equivalent<\/td>\n<\/tr>\n<tr>\n<td>Native forecast resolution<\/td>\n<td>Up to 5 km (EPT-2 HRRR, Europe); native any-\u0394t (arbitrary lead times, no roll-forward error)<\/td>\n<td>9 km (HRES); fixed time steps<\/td>\n<td>About 25 km; fixed 6-hour roll-forward compounds error<\/td>\n<\/tr>\n<tr>\n<td>Update frequency<\/td>\n<td><a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Up to 24x\/day (EPT-2 RR); 15-min actual generation refresh<\/a><\/td>\n<td>2\u20134x\/day<\/td>\n<td>Typically 4x\/day research cadence; no productised operational schedule<\/td>\n<\/tr>\n<tr>\n<td>Natural-language agent<\/td>\n<td>Athena: briefings, benchmarks, backtests, widget generation (about 90 s per query)<\/td>\n<td>None<\/td>\n<td>None<\/td>\n<\/tr>\n<tr>\n<td>Live cross-model benchmarking<\/td>\n<td>25+ models on one platform; any region, any variable; result in seconds<\/td>\n<td>Available to members; no productised cross-vendor surface<\/td>\n<td>No productised benchmarking surface<\/td>\n<\/tr>\n<tr>\n<td>Native power forecasts<\/td>\n<td>Solar, wind on\/offshore, load, residual load in DE, GB, FR, NL, BE; 20-day horizon<\/td>\n<td>Not a native product<\/td>\n<td>Not a native product<\/td>\n<\/tr>\n<tr>\n<td>API \/ SDK<\/td>\n<td>REST + Apache Arrow; <code>pip install jua<\/code>; hindcast access; ENTSO-E integration<\/td>\n<td>Grib files via MARS; member access<\/td>\n<td>Research code \/ limited API<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>European Energy Market Outlook 2026<\/h2>\n<p>The structural direction of European power markets in 2026 is unambiguous. With renewables already at 47.5% of EU consumption, growth in solar and wind capacity continues to reshape price formation. <a href=\"https:\/\/www.pv-magazine.com\/2024\/12\/18\/eu-solar-installations-hit-65-5-gw-in-2024-says-solarpower-europe\/\" target=\"_blank\" rel=\"noindex nofollow\">The solar and wind capacity growth noted earlier<\/a> underpins this shift. <a href=\"https:\/\/eea.europa.eu\/en\/analysis\/indicators\/share-of-energy-consumption-from\" target=\"_blank\" rel=\"noindex nofollow\">The revised Renewable Energy Directive sets a binding EU minimum target of 42.5% renewables in gross final energy consumption by 2030<\/a>, which requires a compound annual growth rate of 8%. That rate is nearly twice the observed 4% rate over the past decade.<\/p>\n<p><a href=\"https:\/\/about.bnef.com\/insights\/clean-energy\/bloombergnefs-new-energy-outlook-2026-transition-to-newer-technologies-expanded-electrification-to-strengthen-nations-energy-security\" target=\"_blank\" rel=\"noindex nofollow\">BloombergNEF&#8217;s New Energy Outlook 2026 projects solar will become the world&#8217;s single largest source of electricity by 2032<\/a>, while battery storage grows substantially by 2035. <a href=\"https:\/\/about.bnef.com\/insights\/clean-energy\/bloombergnefs-new-energy-outlook-2026-transition-to-newer-technologies-expanded-electrification-to-strengthen-nations-energy-security\" target=\"_blank\" rel=\"noindex nofollow\">Rising electricity demand growth is placing increasing pressure on grid investment, system expansion, and permitting timelines<\/a>. That pressure compounds integration challenges for higher renewable penetration. For traders, the result is more weather-driven intraday volatility, tighter balancing windows, and a higher premium on forecast accuracy at short lead times.<\/p>\n<h2>European Power Price Volatility and Forecast Quality<\/h2>\n<p>European wholesale power prices in 2026 remain structurally volatile. The primary drivers are renewable intermittency, gas supply constraints, and demand-side electrification. <a href=\"https:\/\/about.bnef.com\/insights\/clean-energy\/bloombergnefs-new-energy-outlook-2026-transition-to-newer-technologies-expanded-electrification-to-strengthen-nations-energy-security\" target=\"_blank\" rel=\"noindex nofollow\">Recent successive energy market shocks, including Covid-19, the war in Ukraine, and conflict in the Middle East, are pushing European countries to reduce reliance on imported fossil fuels and strengthen energy security<\/a>. This shift accelerates the renewable build-out that drives intraday price swings.<\/p>\n<p>This accelerated build-out explains why <a href=\"https:\/\/about.bnef.com\/insights\/clean-energy\/bloombergnefs-new-energy-outlook-2026-transition-to-newer-technologies-expanded-electrification-to-strengthen-nations-energy-security\" target=\"_blank\" rel=\"noindex nofollow\">electricity is becoming increasingly important in Europe under BloombergNEF scenarios<\/a>, with the transition path running directly through the current decade&#8217;s grid and storage build. As that transition unfolds, price direction in any given week becomes a function of wind and solar output, temperature, and storage levels. Each of those drivers is a forecast problem. The quality of the forecast is the quality of the position.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is Jua for Energy, and how does it differ from a standard weather data subscription?<\/h3>\n<p>Jua for Energy is the first applied product from Jua, a foundation model and agent company. The underlying technology combines EPT-2, a general physics foundation model, with Athena, an AI agent. A standard weather data subscription delivers raw NWP outputs, typically ECMWF or GFS grib files, at two to four runs per day, with no benchmarking surface, no ensemble comparison, and no analyst layer. Jua for Energy delivers a unified workspace with more than 25 models on a single platform, EPT-2 RR refreshing up to 24 times per day, and power forecasts for DE, GB, FR, NL, and BE refreshing every 15 minutes. Athena resolves natural-language queries into briefings, benchmarks, and backtests in about 90 seconds. The platform does not replace an ECMWF subscription, because ECMWF HRES, ENS, and AIFS all run natively on the Jua platform. It replaces the plumbing around it.<\/p>\n<h3>How accurate is EPT-2 compared to ECMWF HRES for European wind and solar forecasting?<\/h3>\n<p>EPT-2 outperforms ECMWF HRES on every lead time from 0 to 240 hours on the four variables that drive European energy P&amp;L: 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation. EPT-2e, the ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time. These results are documented in the peer-reviewed technical report arXiv:2507.09703, benchmarked against more than 10,000 real ground stations using open-source StationBench, with no post-processing or station fine-tuning. A 1 GW wind portfolio that gains four percentage points of forecast accuracy saves about \u20ac1.5 million per year. A 1 GW solar portfolio at the same gain saves about \u20ac3 million per year.<\/p>\n<h3>What does Athena actually do, and how long does it take?<\/h3>\n<p>Athena is Jua&#8217;s AI agent, instrumented with the Jua for Energy tool surface. It accepts a natural-language objective, plans the steps required to resolve it, calls the relevant tools such as forecast queries, model benchmarks, backtests, and widget generation, and evaluates intermediate outputs. The agent then returns a deliverable. A typical briefing or benchmark query resolves in about 90 seconds. A full backtest against years of historical forecasts completes in about 5 minutes. Athena can also auto-create personalised widgets and dashboards on request, which removes the manual assembly step. The agent layer is domain-agnostic by architecture, and the Jua for Energy tool surface is the first instrumentation rather than the last.<\/p>\n<h3>Can quant developers and systematic funds access Jua for Energy programmatically?<\/h3>\n<p>Quant developers and systematic funds can access Jua for Energy programmatically. <code>pip install jua<\/code> installs the Python SDK from PyPI. The REST API exposes more than 25 models through a single schema, with Apache Arrow support for large payloads. Hindcast data is available across multiple Jua and third-party models for backtesting. ENTSO-E grid data integrates directly for European power-market reference data. The developer dashboard is at developer.jua.ai and documentation at docs.jua.ai. Quant teams pipe Jua forecasts directly into their own systematic models, and integrations that take a quarter to build elsewhere typically stand up in days.<\/p>\n<h3>How does Jua for Energy handle model divergence alerts?<\/h3>\n<p>Four alert types run continuously across the 25+ model fleet on the Jua platform. Divergence alerts fire the moment two or more models disagree on a key variable, which signals that a tradeable spread has opened. Correction alerts fire the moment a model revises its own output between runs, which creates a window to act before the market reprices. Threshold alerts fire on user-defined conditions, filterable by zone and PSR type. New model run alerts notify when a specific model&#8217;s latest forecast becomes available. All four arrive as notifications without requiring active platform monitoring, so the trade window opens with a notification instead of a missed move.<\/p>\n<h2>Conclusion: One Workspace, Before the Market Moves<\/h2>\n<p>Fragmented, stale forecasts are a structural cost in European energy trading. With renewables approaching half of EU electricity consumption, with Germany&#8217;s 30-minute gate-closure window, and with BloombergNEF projecting substantial growth in battery storage by 2035, the margin for forecast error and analytics latency compresses every year.<\/p>\n<p>Jua for Energy addresses each failure mode directly. EPT-2 RR refreshes up to 24 times per day against the legacy stack&#8217;s two to four runs. EPT-2 outperforms ECMWF HRES on every lead time and every variable that drives a European energy P&amp;L, as documented in arXiv:2507.09703. EPT-2e&#8217;s ensemble performance against ENS, documented earlier, holds across the same lead-time range. Athena resolves natural-language queries into analyst-grade briefings in about 90 seconds. Divergence and correction alerts surface trade windows before the market reprices. Power forecasts for DE, GB, FR, NL, and BE refresh every 15 minutes. All 25+ models, including ECMWF, GFS, Aurora, GraphCast, AIFS, and the full EPT family, run on one platform, one schema, and one API.<\/p>\n<p>The 7\u20139 a.m. manual prep routine becomes a single workspace that is open before the market. The numbers speak.<\/p>\n<p><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>Run EPT-2 against your current forecast provider on your own region and variables in under 5 minutes.<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Jua delivers up to 24 daily forecast refreshes &amp; live 25-model benchmarking for European energy markets. Beat volatility with AI. Book a demo today.<\/p>\n","protected":false},"author":103,"featured_media":555,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[13],"tags":[],"class_list":["post-556","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product"],"_links":{"self":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts\/556","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=556"}],"version-history":[{"count":0,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts\/556\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media\/555"}],"wp:attachment":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media?parent=556"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/categories?post=556"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/tags?post=556"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}