{"id":572,"date":"2026-06-13T05:00:57","date_gmt":"2026-06-13T05:00:57","guid":{"rendered":"https:\/\/jua.ai\/articles\/europe-renewable-forecast-api-python\/"},"modified":"2026-06-13T05:00:57","modified_gmt":"2026-06-13T05:00:57","slug":"europe-renewable-forecast-api-python","status":"publish","type":"post","link":"https:\/\/jua.ai\/articles\/europe-renewable-forecast-api-python\/","title":{"rendered":"How To Integrate Europe Renewable Forecast APIs in Python"},"content":{"rendered":"<p><em>Written by: Olivier Lam, Physical AI Team, Jua.ai AG<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for Your Python Power Stack<\/h2>\n<ul>\n<li>Multi-vendor renewable forecast stacks in Europe create brittle pipelines that drain engineering time better spent on alpha research.<\/li>\n<li>Jua\u2019s EPT-2 foundation model outperforms ECMWF HRES on every lead time for wind, temperature, and solar radiation variables.<\/li>\n<li>The single Jua Python SDK unifies forecasts, ENTSO-E grid data, hindcasts, and natural-language queries behind one API key and schema.<\/li>\n<li>Native MW power forecasts, 15-minute actual-generation refresh, and rapid hindcast backtests remove separate pvlib conversions and XML parsers.<\/li>\n<li><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>Run a live benchmark on your own region and variables<\/strong><\/a> to see the unified SDK in action.<\/li>\n<\/ul>\n<h2>Prerequisites and Jua Platform Context<\/h2>\n<p>This tutorial assumes Python 3.10 or later, pip, and an ENTSO-E security token (available from the ENTSO-E Transparency Platform). You should already recognise the following terms: <strong>grib<\/strong> (binary format for gridded meteorological data), <strong>NWP<\/strong> (numerical weather prediction, a physics-based atmospheric simulation), <strong>ensemble<\/strong> (a set of perturbed model runs that quantify forecast uncertainty), <strong>lead time<\/strong> (hours between model initialisation and the valid time of a forecast), <strong>dissemination<\/strong> (the moment a model run becomes available to end users), <strong>hindcast<\/strong> (a forecast generated for a past period using only information available at that time, used for backtesting), <strong>RMSE<\/strong> (root mean square error, a deterministic accuracy metric), and <strong>CRPS<\/strong> (continuous ranked probability score, a probabilistic accuracy metric).<\/p>\n<p>Before you start coding, it helps to understand the technology behind the SDK. Jua for Energy is the first applied product of Jua, a foundation model and agent company. The Earth Physics Transformer (EPT) family is a general spatiotemporal transformer foundation model that learns governing physics, such as mass, momentum, and energy conservation, directly from observational data. Athena is Jua&#8217;s AI agent, currently instrumented with the Jua for Energy tool surface. The same architecture will expand to other physical-economy domains.<\/p>\n<h2>Step 1: Install the Jua SDK and Authenticate<\/h2>\n<p>You install the SDK from PyPI and authenticate with your Jua API key. The same client object then exposes both weather forecasts and ENTSO-E grid data under a unified schema, which removes the separate authentication flow that a raw ENTSO-E XML integration requires.<\/p>\n<pre><code># Install pip install jua # authenticate_and_forecast.py from jua import JuaClient client = JuaClient(api_key=\"YOUR_JUA_API_KEY\") # Pull a deterministic EPT-2 wind forecast for Germany # and ENTSO-E actual generation in a single session forecast = client.forecast( model=\"ept-2\", variables=[\"wind_speed_100m\", \"surface_solar_radiation\"], region=\"DE\", horizon_hours=48, ) grid = client.entsoe( country=\"DE\", data_type=\"actual_generation\", ) print(forecast.head()) print(grid.head()) <\/code><\/pre>\n<p>Authentication uses a single API key, which removes the need for a separate ENTSO-E XML parser. Because both forecast and grid data share the same authentication flow and schema, the <code>grid<\/code> object returns generation by PSR (Production Source Resource) type, such as wind onshore, wind offshore, and solar, in the same pandas-compatible format as the forecast object. You avoid schema translation work between sources.<\/p>\n<h2>Step 2: Retrieve Unified Solar and Wind Forecasts in MW<\/h2>\n<p>Jua for Energy&#8217;s Power Forecast surface converts EPT weather variables to MW natively using installed-capacity data and a Fundamental Model that runs to 20 days. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Actual generation refreshes every 15 minutes with a 48-hour horizon<\/a>, which matches the cadence European intraday markets trade at. EPT2-HRRR produces forecasts at approximately 5 km resolution over Europe.<\/p>\n<pre><code># power_forecast.py from jua import JuaClient import pandas as pd client = JuaClient(api_key=\"YOUR_JUA_API_KEY\") # MW-scale solar and wind forecast, no pvlib conversion required power = client.power_forecast( country=\"DE\", psr_types=[\"solar\", \"wind_onshore\", \"wind_offshore\"], model=\"ept-2\", horizon_hours=48, refresh=\"actual\", # 15-minute actual-generation refresh ) print(power[[\"valid_time\", \"psr_type\", \"generation_mw\"]].head(20)) <\/code><\/pre>\n<p>This example shows how to retrieve native MW forecasts without a separate pvlib conversion step. The <code>refresh=\"actual\"<\/code> parameter routes the query to the Actual Generation Model. Switching to <code>refresh=\"fundamental\"<\/code> returns the 20-day Fundamental Model output. Both share the same schema, so you do not change the pipeline when you move between intraday and multi-day horizons.<\/p>\n<h2>Step 3: Run a Hindcast Backtest for Model Validation<\/h2>\n<p><a href=\"https:\/\/solaranywhere.com\/support\/forecast-data\/hindcasts\" target=\"_blank\" rel=\"noindex nofollow\">Hindcast data enables pre-deployment evaluation of operational forecast uses and can reduce capital costs and project risk<\/a>. Jua for Energy exposes hindcasts across multiple EPT and third-party models through the same SDK call. <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2e beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time<\/a>, a result validated against more than 10,000 real ground stations on open-source StationBench with no post-processing.<\/p>\n<pre><code># hindcast_backtest.py from jua import JuaClient import pandas as pd client = JuaClient(api_key=\"YOUR_JUA_API_KEY\") # Pull two years of hindcast data for 100m wind over Germany hindcast = client.hindcast( model=\"ept-2e\", variables=[\"wind_speed_100m\"], region=\"DE\", start=\"2023-01-01\", end=\"2024-12-31\", lead_time_hours=24, ) # Compare against ECMWF ENS hindcast on the same period ecmwf_hindcast = client.hindcast( model=\"ecmwf-ens\", variables=[\"wind_speed_100m\"], region=\"DE\", start=\"2023-01-01\", end=\"2024-12-31\", lead_time_hours=24, ) # Compute RMSE for both from sklearn.metrics import mean_squared_error import numpy as np rmse_ept = np.sqrt(mean_squared_error( hindcast[\"observed\"], hindcast[\"forecast\"], )) rmse_ecmwf = np.sqrt(mean_squared_error( ecmwf_hindcast[\"observed\"], ecmwf_hindcast[\"forecast\"], )) print(f\"EPT-2e RMSE: {rmse_ept:.3f} m\/s\") print(f\"ECMWF ENS RMSE: {rmse_ecmwf:.3f} m\/s\") <\/code><\/pre>\n<p>You can run a full two-year backtest across multiple variables and regions via Athena or programmatically through the SDK as shown above. The Apache Arrow payload format handles continental, multi-variable queries without choking on payload size, which often breaks REST APIs that lack large-payload support.<\/p>\n<h2>Step 4: Use Athena for Natural-Language Queries<\/h2>\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 required tool calls, and returns a briefing, benchmark, backtest, or custom widget. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Athena turns raw physics predictions from EPT-2 into actionable analysis by reading market context and modelling participant behaviour<\/a>. Typical queries resolve in approximately 90 seconds.<\/p>\n<pre><code># athena_query.py from jua import AthenaClient athena = AthenaClient(api_key=\"YOUR_JUA_API_KEY\") # Natural-language query, returns a structured briefing response = athena.query( \"What is the 100m wind forecast spread across EPT-2e and ECMWF ENS \" \"for northern Germany over the next 72 hours, \" \"and where do the models diverge most?\" ) print(response.briefing) print(response.widget_url) # auto-generated dashboard widget <\/code><\/pre>\n<p>The same Athena interface accepts backtest requests. For example, <code>\"Backtest a wind-ramp strategy on EPT-2e over the last two winters for DE and GB\"<\/code> returns a full backtest report in approximately 5 minutes. You do not need to build a separate pipeline.<\/p>\n<h2>Methods, Frameworks, and SDK Comparison<\/h2>\n<p>The table below compares the three most common components of a Europe renewable forecast stack against Jua for Energy as a unified SDK. Every data point is cited inline.<\/p>\n<table>\n<thead>\n<tr>\n<th>Capability<\/th>\n<th>Meteomatics<\/th>\n<th>Open-Meteo<\/th>\n<th>Jua for Energy<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>SDK count required<\/td>\n<td>Separate Meteomatics API client<\/td>\n<td>Separate Open-Meteo client or requests wrapper<\/td>\n<td>Single <code>pip install jua<\/code> SDK covering forecasts, ENTSO-E grid data, hindcasts (natural-language queries handled by Athena)<\/td>\n<\/tr>\n<tr>\n<td>Hindcast access<\/td>\n<td>Limited historical forecast archive; no native backtesting surface<\/td>\n<td>Historical weather data available; no productised hindcast with operational delays modelled<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">Hindcast data available across multiple EPT and third-party models; EPT-2e beats ECMWF ENS mean on RMSE and CRPS at virtually every lead time<\/a><\/td>\n<\/tr>\n<tr>\n<td>Actual-generation refresh<\/td>\n<td>Hourly updates<\/td>\n<td>Hourly updates<\/td>\n<td><a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">15-minute actual-generation refresh with 48-hour horizon<\/a><\/td>\n<\/tr>\n<tr>\n<td>Update cadence<\/td>\n<td>Hourly updates<\/td>\n<td>Hourly updates<\/td>\n<td>Multiple updates per day for rapid-refresh variants<\/td>\n<\/tr>\n<tr>\n<td>Spatial resolution (forecasts)<\/td>\n<td>Dependent on underlying NWP source<\/td>\n<td>Dependent on underlying NWP source<\/td>\n<td>Native ~5 km resolution over Europe (EPT2-HRRR)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The structural advantage of the unified SDK goes beyond convenience. When Meteomatics, Open-Meteo, and ENTSO-E sit as separate clients, a schema change in any one of them can break the downstream MW conversion. With Jua for Energy, the schema stays stable across forecast, grid, and hindcast data because they share a single API contract.<\/p>\n<h2>Common Challenges and Troubleshooting<\/h2>\n<p><strong>Authentication.<\/strong> The most common failure in multi-API stacks is credential rotation, where ENTSO-E tokens expire, Meteomatics keys rotate, and Open-Meteo rate limits change without notice. Each failure demands separate debugging and redeployment. The Jua for Energy SDK removes this maintenance burden by using a single API key for all surfaces, including the ENTSO-E integration. You rotate once and every surface updates.<\/p>\n<p><strong>Payload size.<\/strong> Continental, multi-variable, multi-model queries return large arrays. The Jua for Energy REST API supports Apache Arrow as a columnar payload format, which handles the data volumes required for European-scale backtests without the memory pressure that JSON-over-HTTP produces at scale. Pass <code>format=\"arrow\"<\/code> in the SDK call for any query covering more than a single country or more than 48 hours of hindcast data.<\/p>\n<p><strong>Timezone handling.<\/strong> European power markets operate in local time (CET\/CEST), but NWP models disseminate in UTC. The Jua for Energy SDK returns all timestamps in UTC by default. Pass <code>tz=\"Europe\/Berlin\"<\/code> or any IANA timezone string to the forecast or hindcast call to receive market-aligned timestamps without a separate conversion step. <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">A typical Jua run completes approximately 2.5 hours ahead of competing operational runs at the same cycle<\/a>, so the dissemination timestamp in UTC matters for latency-sensitive pipelines.<\/p>\n<h2>Measuring Success of Your Migration<\/h2>\n<p>Three metrics define a successful migration from a multi-API stack to the Jua for Energy SDK. These metrics capture the two dimensions that matter most for production trading systems: forecast accuracy and operational speed.<\/p>\n<p><strong>RMSE and CRPS reduction.<\/strong> Run the hindcast backtest in Step 3 against your current provider on the region and variable that drives your highest-stakes position. <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2e beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time across 0\u2013240 hours<\/a>. The benchmark is reproducible on your own data in approximately 5 minutes.<\/p>\n<p><strong>Backtest runtime.<\/strong> A full two-year, multi-variable backtest via Athena completes in approximately 5 minutes. The equivalent workflow on a manually assembled hindcast archive typically takes hours or days, depending on data availability and pipeline stability.<\/p>\n<p><strong>Dissemination advantage.<\/strong> <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">A typical Jua run completes approximately 2.5 hours ahead of competing operational runs at the same cycle<\/a>. For intraday power trading, that window often contains the trade.<\/p>\n<p><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>Measure these metrics yourself with a live benchmark<\/strong><\/a> on your own region and variables.<\/p>\n<h2>Advanced Model Choices and Scaling<\/h2>\n<p><strong>Ensemble depth with EPT-2e.<\/strong> <a href=\"https:\/\/arxiv.org\/abs\/2410.15076\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2e is the ensemble variant of the EPT family, with a 60-day forecast horizon<\/a>. It suits probabilistic power forecasting, CRPS-optimised positions, value-at-risk calculations on generation shortfall, and any strategy that needs a distribution over outcomes rather than a single deterministic trace. Pass <code>model=\"ept-2e\"<\/code> in any SDK call to switch from deterministic to ensemble output.<\/p>\n<p><strong>Rapid-refresh variants.<\/strong> EPT-2 RR is a rapid-refresh variant. For intraday strategies where the forecast from two hours ago already feels stale, set <code>model=\"ept-2-rr\"<\/code> and subscribe to correction alerts via the Jua platform. These alerts fire the moment a model revises its own output between runs and surface the trade window before the market reprices.<\/p>\n<p><strong>Scaling to additional European countries.<\/strong> Jua for Energy currently provides live power forecasts for Germany, Great Britain, France, the Netherlands, and Belgium, with additional country coverage added on a weekly basis. The SDK call is identical across countries, so you only change the <code>region<\/code> parameter. ENTSO-E integration covers the full European grid, so actual-generation data is available for any country in the ENTSO-E transparency dataset, even when a dedicated power-forecast surface does not yet exist for that market.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How long does it take to replace an existing multi-API stack with the Jua for Energy SDK?<\/h3>\n<p>Most quant teams stand up the integration in days, not a quarter. The SDK installs with a single pip command, authentication uses a single API key, and the schema stays consistent across forecast, hindcast, and ENTSO-E grid data. The engineering work that typically consumes a quarter elsewhere, such as building the ingestion pipeline, the ensemble logic, the benchmarking harness, and the hindcast access, is handled by the SDK. A live benchmark against your current provider runs in approximately 5 minutes from first installation.<\/p>\n<h3>Do I need meteorology expertise to use the Jua for Energy Python SDK?<\/h3>\n<p>No. The SDK abstracts the NWP and grib layer entirely. Quant developers and data engineers work with pandas-compatible DataFrames and standard Python types. Meteorology expertise helps when you interpret ensemble spread and select the right model variant for a given lead time, but it is not required to retrieve forecasts, run hindcasts, or query Athena. Teams with in-house meteorologists can use the SDK alongside the Jua platform&#8217;s benchmarking surface to run rigorous head-to-head model comparisons on their own region and variable.<\/p>\n<h3>What hindcast coverage is available for backtesting European renewable strategies?<\/h3>\n<p>Hindcast data is available across multiple EPT and third-party models. ERA5 reanalysis data is available from 1990 onward at hourly cadence. EPT-2e hindcasts are available for backtesting probabilistic strategies. All hindcasts are generated using only information available at each historical time step, including operational delays, so backtest results reflect the forecast quality a live system would have delivered rather than a post-hoc reconstruction. A full multi-year backtest runs in approximately 5 minutes via Athena.<\/p>\n<h3>How does the ENTSO-E integration work, and what data does it expose?<\/h3>\n<p>The Jua for Energy SDK includes a direct ENTSO-E integration that exposes actual generation, installed capacity, and PSR (Production Source Resource) classifications, such as wind onshore, wind offshore, solar, and others, across the European power markets covered by the ENTSO-E Transparency Platform. The integration uses the same authentication flow as the rest of the SDK, a single Jua API key, returns data in the same schema as the forecast and hindcast surfaces, and refreshes every 15 minutes for actual-generation data. You avoid maintaining a separate ENTSO-E XML parser.<\/p>\n<h3>When should a team automate Athena queries versus using the SDK directly?<\/h3>\n<p>Athena suits ad-hoc analysis, such as morning briefings, model divergence checks, backtest requests phrased in natural language, and custom widget generation. Typical queries resolve in approximately 90 seconds and backtests in approximately 5 minutes. The SDK suits production pipelines, such as scheduled forecast pulls, systematic strategy signals, and any workflow where schema stability and programmatic control matter more than natural-language flexibility. Most teams use both, with Athena for research and briefing workflows and the SDK for the production signal pipeline.<\/p>\n<h2>Conclusion: From Fragmented Stack to Single SDK<\/h2>\n<p>A Europe renewable forecast stack built on Meteomatics, Open-Meteo, pvlib, and raw ENTSO-E XML means four maintenance contracts, four authentication flows, and four points of failure. The Jua for Energy Python SDK replaces that stack with a single <code>pip install jua<\/code>. You gain EPT-2 and EPT-2e forecasts at native 5 km resolution, ENTSO-E grid data, 15-minute actual-generation refresh, hindcasts for backtesting, and Athena natural-language queries, all under one schema and one API key.<\/p>\n<p>The accuracy gains documented throughout this tutorial translate directly to portfolio value. A 1 GW wind portfolio that gains four percentage points of forecast accuracy saves approximately \u20ac1.5 M per year. Run the benchmark on your own region and variable to quantify the impact. Jua is a foundation model and agent company, and Jua for Energy is the first applied product. The stack you replace today is the plumbing around a forecast that was already stale. <a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>Replace your stale stack with a live integration test<\/strong><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Stop juggling multi-vendor APIs. Jua&#8217;s single Python SDK unifies renewable forecasts, ENTSO-E data &amp; hindcasts \u2014 with accuracy that beats ECMWF HRES.<\/p>\n","protected":false},"author":103,"featured_media":571,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[11],"tags":[],"class_list":["post-572","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\/572","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=572"}],"version-history":[{"count":0,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts\/572\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media\/571"}],"wp:attachment":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media?parent=572"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/categories?post=572"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/tags?post=572"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}