IEA Wind TCP Task 51 "Forecasting for the weather driven energy system" - 2nd Austrian Workshop
Bibliographic record
Abstract
The 2nd IEA Task 51 Austria Workshop “Weather-Driven Extreme Events in the Energy System” was held on 11 November 2025 at the Austro Tower in Vienna and brought together 125 experts from the energy sector, grid operation, meteorology, research, and public authorities. The workshop focused on the growing risks posed by weather-driven extreme events and their impacts on renewable energy generation, electricity markets, grid stability, and operational decision-making. The event was organized by GeoSphere Austria in cooperation with Austro Control Digital Services, with support from the Austrian Federal Ministry for Innovation, Mobility and Infrastructure (BMIMI) and the Austrian Research Promotion Agency (FFG). The programme covered a broad spectrum of topics, including: Extreme weather processes in the Alpine region and impacts on energy infrastructure Forecast errors and the limits of predictability Early warning systems and operational decision support Challenges in energy trading and grid management Digital twins, nowcasting systems, AI-driven forecasting, and meteorological risk indicators Weather-induced impacts on critical infrastructure (transmission and distribution grids, Austrian Railways, airport operations) This Zenodo record contains: Workshop programme (PDF) All presentations (PDF) as individual files Outputs from the interactive sessions (A–D) Optional: A ZIP archive containing all presentations in a single package All materials are part of the national Austrian activities within IEA Wind TCP Task 51: Forecasting for the Weather-Driven Energy System. Contributors (Presenters) Sabine Mitter (BMIMI) Bernhard Niedermoser (GeoSphere Austria) Florian Mader (WEB Windenergie) Gernot Waldsam (Austro Control Digital Services) Lukas Strauss (Austro Control Digital Services) Irene Schicker (GeoSphere Austria) Philipp Piber (Austro Control) Horst Brandlmaier (OeMAG) Short Talks – Energy Sector Andreas Nehls (Burgenland Energie Trading) Andreas Forster (oekostrom AG) Anna-Maria Tilg (Austrian Power Grid) Bernhard Spitzer (Energienetze Steiermark) Robert Grassinger (ÖBB Infrastruktur) Philipp Geier (Energie Steiermark Green Power) Short Talks – Research & Weather Laura Essl (Disaster Competence Network Austria – DCNA) Irene Schicker (GeoSphere Austria) Clemens Weiß (ACDS) Anton Fuxjäger (Enlite AI) Michael Kernitzkyi (JOANNEUM RESEARCH) Interactive Sessions Session A + D: Lukas Strauss Session B + C: Irene Schicker
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.043 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".