Uncertainty Estimation in Wind Power Forecasts Using Monte Carlo Simulations
Bibliographic record
Abstract
In our earlier paper [9] we took the histories of wind speed forecasts and actual wind speed data available from Environment Canada and presented that the hourly wind speed forecast error distributions are nearly Gaussian in nature. In this paper we used the hourly error distribution to model a representative wind-speed realization as the sum of a deterministic term and a stochastic term. The deterministic term was the forecast provided by Environment Canada, while the stochastic component, the error in the forecast, was modeled as a first-order gaussian markov process. Wind-speed realizations were. Then input to a wind generator model developed in MATLAB®/Simulink®to get wind power realizations. The uncertainties in the wind speed-realizations were transferred to the wind power realizations as well. Monte Carlo Simulations were performed to assess likely range of wind power production. It is shown that how the statistics of wind power prediction obtained by performing Monte Carlo Simulation gave an idea of the risk involved in wind power production.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".