The IEA Wind TCP Task 51 Austria - Stakeholder interaction and priorities for forecasts
Bibliographic record
Abstract
This work presents key findings from the first Austrian workshop of IEA Wind TCP Task 51 on "Forecasting for the Weather-Driven Energy System", which brought together 120 participants from over 50 organizations. Through structured stakeholder engagement, the workshop revealed critical priorities for advancing renewable energy forecasting in complex terrain.Results highlight the continued dominance of day-ahead forecasting (56% of respondents), while identifying growing needs in extreme weather prediction (85% concerned) and artificial intelligence integration (rated 4.35/5 in importance). On the other hand, a number of gaps were identified related to the awareness of extremes and uncertainty and the knowledge and implementation status of such forecast tools. The Alpine context presents unique challenges, where complex terrain and cross-border power flows create specific forecasting requirements. Based on stakeholder feedback, two follow-up workshops will be organised focusing on extreme events and integrated forecasting solutions.This study provides concrete guidance for developing next-generation forecasting systems and demonstrates the value of structured stakeholder engagement in shaping forecasting solutions for the energy transition.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".