Sequential methods for error correction of probabilistic wind power forecasts
Bibliographic record
Abstract
Reliable probabilistic production forecasts are required to better manage the uncertainty that the rapid build-out of wind power capacity adds to future energy systems. In this article, we consider sequential methods to correct errors in wind power production forecast ensembles derived from numerical weather predictions. We propose combining neural networks with time-adaptive quantile regression to enhance the accuracy of wind power forecasts. We refer to this approach as Neural Adaptive Basis for (time-adaptive) Quantile Regression or NABQR. First, we use NABQR to correct power production ensembles with neural networks. We find that Long Short-Term Memory networks are the most effective architecture for this purpose. Second, we apply time-adaptive quantile regression to the corrected ensembles to obtain optimal median predictions along with quantiles of the forecast distribution. With the suggested method, we beat state-of-the-art methods and achieve accuracy improvements up to 40% in mean absolute terms in an application to day-ahead forecasting of on- and offshore wind power production in Denmark. In addition, we explore the value of our method for applications in energy trading. We have implemented the NABQR method as an open-source Python package to support applications in renewable energy forecasting and future research. • Developed neural networks for data-driven updates of wind power ensembles. • Applied time-adaptive quantile regression on corrected ensembles. • Improved wind power production forecasts by up to 40% in a mean absolute sense. • Published well-documented open-source Python package: nabqr
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".