Wind energy forecast error estimation using black & scholes mathematical model
Bibliographic record
Abstract
Renewable sources of energy such as wind, have been focus of many studies in recent years. The clean and economic nature of renewable resources makes them very appealing candidates for the future's energy planning, however the uncertain nature of them makes the integration process complicated. One method of dealing with this uncertainty is to use new storage technologies and the other way is to procure reserve dedicated to these renewable sources of energy. This paper suggests a method for wind producers to buy reserve from other non-renewable sources to mitigate the uncertainty. This paper investigates the usage of Black and Scholes mathematical model not only for pricing the options but also for estimating the amount of possible errors in wind energy forecast for a future time span. As far as the authors are aware of, such a novel approach has not yet been investigated. This method utilizes the concept of historic volatility in finance and introduces the historic volatility of wind energy which can be used for wind energy forecast error estimation. Further to the authors' previous research on the use of “binomial tree” as both financial and energy forecast model, this research applies the Black and Scholes model and reports on the advantages of the latter.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".