Wind Generation · DE/LU

Wind power in the German electricity system

Wind is the backbone of German renewable generation. Onshore wind is the largest renewable technology in the country by generated volume, complemented by a growing offshore fleet in the North Sea and Baltic Sea. During strong wind periods, wind power alone can cover a substantial share of national electricity demand, and it dominates the residual load position of the system in the winter months.

Geographically, onshore capacity is concentrated in the north and east of Germany: Lower Saxony, Schleswig-Holstein, and Brandenburg host the largest fleets, while expansion in the south has historically lagged behind. Offshore wind farms connect to the transmission grids of TenneT in the North Sea and 50Hertz in the Baltic Sea. Together with Amprion in the west and TransnetBW in the southwest, these four transmission system operators are responsible for integrating German wind feed-in into the grid.

Official data on German wind feed-in is published by the TSOs on the joint platform Netztransparenz, by the Bundesnetzagentur on SMARD, and on the ENTSO-E Transparency Platform. The complete registry of German generation units is maintained in the Marktstammdatenregister.

Why wind generation forecasts for DE/LU are important

Wind feed-in is a major source of uncertainty in the German short-term power markets. Unlike solar, wind generation carries no predictable daily shape: it can run near full capacity for days and then collapse within hours as a front passes. These swings move day-ahead prices, drive intraday volatility, and influence whether the system is long or short. Wind forecast quality is therefore a valuable input across short-term markets.

On the volume side, the stakes are just as high. Direct marketers of wind portfolios depend on accurate forecasts to nominate correctly and avoid balancing costs. Balancing group managers need them to control imbalance exposure in a system where deviations are settled at increasingly volatile imbalance prices. Storage and flexibility operators use wind forecasts to position themselves ahead of price ramps that wind swings trigger.

Telescope Energy provides aggregated wind generation forecasts for the DE/LU bidding zone for these use cases.

How our forecast works

Telescope Energy operates a collaborative forecasting platform. Our proprietary ensemble engine combines models developed by our research team with forecasts from independent expert forecasters. Each contributing model is continuously evaluated against realized feed-in, and the ensemble weights individual signals by their recent performance. The combined signal is designed to use complementary model strengths and reduce dependence on any single model.

The underlying models process high-resolution numerical weather predictions from multiple weather providers and learn the relationship between wind conditions and actual feed-in across the German onshore and offshore fleet. Forecasts are updated frequently as new weather model runs become available, so recent wind field information can be reflected in each update. Forecasts are delivered through the Telescope API for integration into existing trading and nomination workflows.

What drives German wind generation?

Wind speed is the dominant driver, and its effect is strongly non-linear: power output rises roughly with the cube of wind speed in the mid range, which means small errors in the wind forecast translate into large errors in the generation forecast. At the upper end, storms introduce an additional discontinuity, as turbines cut out at high wind speeds for protection and national feed-in can drop sharply while the wind is strongest. Timing errors are a further challenge: a front arriving a few hours earlier or later than predicted shifts gigawatts of feed-in between delivery periods.

Beyond the raw wind field, turbine-level effects matter. Wake losses within large wind farms and between neighboring offshore clusters reduce output below what wind speeds alone would suggest. Icing can suppress generation in cold conditions, and maintenance seasons affect availability. On the system side, curtailment plays a growing role: wind feed-in in the north regularly exceeds transmission capacity toward demand centers in the south, so redispatch and grid congestion can push realized generation below the meteorologically possible level. Finally, continuous capacity expansion and repowering of older sites mean the same weather situation yields more energy every year, a structural drift forecasting models must track.

Capturing this combination of non-linear physics, discrete events, and slow structural change is what an ensemble of diverse, continuously benchmarked models is designed to do.

Forecast accuracy

Accuracy claims are easy to make and hard to verify, which is why Telescope Energy is built around transparency. We publish historical forecasts alongside realized wind feed-in, free of charge, so that every prospective customer can benchmark our DE/LU wind generation forecast against their current provider or internal models before committing to anything.

Our ensemble approach is designed for consistent accuracy across weather regimes, including the situations where forecasts matter most: storm passages with cut-out risk, fast-moving fronts, and days where weather models diverge. Because the ensemble reweights contributing models based on ongoing performance, the forecast automatically keeps pace with the expansion and repowering of the German wind fleet, without depending on any single model remaining state of the art.

Frequently asked questions

What does this forecast include?

The product provides quarter-hourly P10, P50, and P90 wind generation forecasts in MW from D+0 through D+7.

How often is the forecast updated?

The forecast runs in rolling mode so new weather-model information can be incorporated throughout the day.

How is the forecast delivered?

Forecasts are delivered through the Telescope API for integration into trading, nomination, analytics, and asset-optimization workflows. Request a pilot to evaluate the product with your own benchmark and use case.