EGU24-17204, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Improving Renewable Energy Forecasting with Meteomatics EURO1k Model

Julie Thérèse Pasquier, Johannes Rausch, Matthias Piot, Julia Schmoeckel, Marco Thaler, Christian Schluchter, and Martin Fengler
Julie Thérèse Pasquier et al.
  • Meteomatics, St. Gallen, Switzerland (

The production of renewable energy from wind and solar sources is intricately linked to meteorological conditions, where wind speed and solar radiation play critical roles. Due to the success of renewable energies, wind turbines are increasingly placed in sites with complex terrain, while solar panels are increasingly situated in alpine areas. However, current weather models often struggle to accurately forecast the weather, especially over complicated topography, due to limitations in spatial resolution. This leads to inaccurate predictions of power production, impacting the efficiency and reliability of renewable energy systems. To address this challenge, Meteomatics developed the EURO1k model, the first pan-European weather model with a 1 km² spatial resolution, providing optimal forecasting for wind and solar power.

The EURO1k model offers a 48-hour forecast horizon, generating a new forecast every hour. In addition to standard data sources such as weather stations, radar, satellite data, and radiosondes, the EURO1k model also incorporates data from a network of Meteodrones - small, unmanned aircraft systems developed by Meteomatics - which collect vertical atmospheric profiles up to 6000m in altitude. The high resolution of the EURO1k model enables accurate representation of small-scale weather patterns, resulting in highly accurate and precise forecasts.

Meteomatics uses a forecast system that combines various global and regional weather models to predict wind and solar power, aiming to reduce average errors. Recently, EURO1k has been integrated into this system, improving intraday and day-ahead power production forecasts. The normalized root mean square error (nRMSE) was reduced by up to 8.1% for intraday and by up to 8.5% for the day-ahead wind power forecast. Furthermore, a comparison of day-ahead forecasts with actual production data, combined with balancing energy costs, demonstrates improved earnings with the addition of the EURO1k model. Indeed, the EURO1k shows especially better performance in weather situations with large uncertainties. This underscores the added value of EURO1k in power forecasting, enhancing the cost efficiency of renewable energies and fostering greater integration into the energy mix, thereby reducing CO2 emissions.

How to cite: Pasquier, J. T., Rausch, J., Piot, M., Schmoeckel, J., Thaler, M., Schluchter, C., and Fengler, M.: Improving Renewable Energy Forecasting with Meteomatics EURO1k Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17204,, 2024.